Nottinghamshire Police deployed live facial recognition technology in Nottingham city centre for the first time today, Friday 21 August.
The marked police van, equipped with a 360-degree dual-camera system, was stationed in Old Market Square as officers monitored the surrounding area.
The cameras scan the faces of people passing the vehicle and compare them with a police watchlist containing images of individuals suspected of serious offences or wanted for safeguarding reasons.
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When the system identifies a potential match, an alert is sent to officers, who must review it and decide whether to approach the person. Police say scans that do not produce a match are deleted within seconds and are not retained.
Signs were displayed around the deployment area informing members of the public that the technology was operating.
The launch follows the force’s announcement in July that facial recognition camera vans would be deployed across Nottinghamshire.
The Wire captured this video and pictures of the technology operating in Old Market Square during its first deployment.
Aug 22, 2026 · via westbridgfordwire.com
From Fuzozo to Pophie: What AI Companion Devices Teach Us About Building the Next Generation of IoT Products Lawrence Wu Lawrence Wu For decades, connected devices have been built around commands. A user presses a button. A sensor reports a reading. A mobile app sends an instruction. Even voice assistants largely followed the same pattern by waiting for a wake word before processing a request. A new generation of AI-native devices is changing that interaction model. Products like AI companions, consumer robots, assistive technologies, educational devices, and wellness products are expected to hold natural conversations, remember previous interactions, recognize different users, respond emotionally, and stay available throughout the day. Instead of reacting to isolated commands, they participate in continuous interactions. That seemingly small shift fundamentally changes the engineering requirements behind the device. Building these experiences is no longer just about integrating a language model. Developers must solve a new class of real-time systems problems involving latency, speech recognition, interruption handling, identity, memory, synchronization, and global infrastructure. Two recent products illustrate this particularly well: Robopoet's Fuzozo and InsBotics' Pophie. Although they target different audiences, they reveal a broader lesson about where IoT architecture is heading. When users are speaking naturally, every pause becomes noticeable. Conversations involve overlapping speech, interruptions, changing speakers, emotional tone, background noise, and long-running context. Unlike issuing a command to a device, conversation is continuous and highly sensitive to timing. The engineering challenge shifts from transmitting data efficiently to maintaining a believable interaction. Instead of optimizing only network throughput or cloud connectivity, developers must optimize something much harder: the flow of human conversation. Most engineers understand that lower latency improves responsiveness. For conversational devices, latency affects something deeper. It shapes whether an interaction feels natural. People instinctively expect conversations to flow without awkward pauses. Delays that might be
Aug 22, 2026 · via iotforall.com
Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)). Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*). Lightweight neural networks are increasingly used for image classification in scenarios where memory, computation, and energy resources are limited. However, reducing model size often results in a noticeable loss of robustness and generalization, as compact architectures struggle to learn fine-grained structural cues such as edges, textures, and high-frequency patterns. This limitation is especially evident in domains like medical imaging, microscopy, and remote sensing, where subtle spatial variations play an important role in discrimination. In this work, we introduce LiFGANet, a lightweight frequency and gradient aware network designed to mitigate this problem by incorporating explicit inductive biases into feature learning. Instead of increasing model capacity, LiFGANet emphasizes structurally informative representations through an efficient frequency gradient interaction mechanism, combined with sparse feature refinement and multi-stage feature aggregation. The proposed design remains compact, with approximately 1.13M parameters, and introduces negligible inference overhead. We evaluate LiFGANet on PneumoniaMNIST, BloodMNIST, FashionMNIST, and EuroSAT. The results show consistent and well-balanced performance across datasets surpassing state-of-the-art lightweight models with similar or larger parameter budgets. Ablation studies further demonstrate the various advantages of frequency guidance and gradient modulation. The code and implementation details are available on GitHub. Show AI generated summary Abstract Dive into the groundbreaking LiFGANet architecture, a lightweight neural network designed to revolutionize image classification in resource-constrained environments. This article explores how LiFGANet tackles the fundamental limitations of compact models by embedding physics-inspired inductive biases that explicitly guide learning toward structurally meaningful features. Discover how
Aug 22, 2026 · via springerprofessional.de
Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)). Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*). This study presents a novel approach to classify five skateboarding tricks (Kickflip, Frontside-180, Nollie Frontside Shove-it, Pop Shove-it, and Ollie) using transfer learning models integrated with Support Vector Machine (SVM) classification. As skateboarding continues to gain prominence in competitive sports, including its Olympic debut, there is increasing demand for objective evaluation systems. The methodology captures skateboarding trick sequences using a YI action camera positioned 1.26m from the performance area and extracts image frames at 30fps. By overall of approximately 750 images were extracted and then would proceed through a train, validation, and test split of 60:20:20 ratio, respectively. Four pre-trained CNN architectures (NasNetLarge, NasNetMobile, MobileNetV2, and MobileNet) were evaluated as feature extractors coupled with SVM classification. Comprehensive evaluation revealed that NasNetLarge achieved the highest classification accuracy of 93% on the test dataset, followed by NasNetMobile (92%), MobileNetV2 (91%), and MobileNet (87%). Confusion matrices indicate specific patterns of misclassification between similar tricks. This objective evaluation system provides a foundation for automated trick recognition in competitive skateboarding, offering potential applications for objective judging systems in competitions and as a training tool for skateboarders seeking performance improvement. Show AI generated summary Abstract This chapter presents a groundbreaking method for classifying skateboarding tricks using advanced image processing and machine learning techniques. The study begins by outlining the growing popularity of skateboarding and the need for objective evaluation methods, especially as the sport gains Olympic recognition. Researchers developed a three-phase experimental setup: capturing trick
Aug 22, 2026 · via springerprofessional.de
Debate sparks among residents in Town of Irmo about flock cameras IRMO, S.C. (WACH) — Some new traffic cameras in the Town of Irmo are causing a bit of controversy for some people who live there. The cameras are making a noticeable presence in the town, as well as across the state. "If it's being used for other means, you know, following people or things outside of that, I really don't see a use for it," David Cope, Lexington County resident, said. Town residents have been showing mixed emotions after Irmo officials announced plans to install over 20 new flock cameras. RELATED | Irmo officials address online misinformation after safety camera expansion "This place is pretty mellow, but lets keep it that way," Robert Aguilera, Town of Irmo resident, said. According to the Town of Irmo website, the cameras are strictly to be used as investigative tools to help law enforcement. The cameras take a picture of the rear of a car to read the license plate, and do not use facial recognition technology. Town councilmember Gabriel Penfield is the only town council chairholder who opposes the additional flock cameras. "We have a level population, we have a fully staffed police department, so I was just very curious of the need for identification came from," Penfield said. The Midlands have seen flock cameras cause issues over the last month, Including two cameras in Irmo being vandalized last week and a former Richland County deputy being arrested after misusing the camera system. Penfield says he agrees with the people who call these cameras an invasion of privacy. "It is a lack of consent for having that information collected and then ultimately stored in public company servers," Penfield said. Stephen Miano, owner of that computer store, says after the cameras collect a
Aug 22, 2026 · via abcnews4.com
Abstract Neuromorphic vision functionalities have been realized by coupling quasi-bound states in the continuum (quasi-BICs) to multiple quantum wells (MQWs). The engineered leaky modes enhance infrared absorption and generate coexisting nonlinear and linear photoresponses that support image preprocessing and in-sensor computing. This approach highlights a new role for quasi-BIC leakage in integrated optoelectronic intelligence. Biological vision systems perform substantial information processing before signals reach the brain1. Within the retina, operations such as contrast enhancement, noise suppression and feature extraction are executed locally, reducing the computational burden of higher visual centers and enabling highly efficient perception. Reproducing such front-end processing capabilities in artificial hardware has become a central objective of neuromorphic vision technologies, which seek to overcome the latency and energy costs associated with conventional von Neumann architectures where sensing and computation are physically separated2,3. Among various optoelectronic platforms, multiple quantum wells (MQWs) provide a promising route for infrared neuromorphic vision systems due to their strong quantum confinement effects, fast carrier dynamics, and compatibility with mature semiconductor fabrication technologies4,5,6,7. These structures have been widely explored in infrared imaging, optical communication, and sensing applications, making them attractive candidates for in-sensor visual processing8,9,10. However, the optical response of MQWs is fundamentally governed by intersubband selection rules, which require a dominant out-of-plane electric-field component to efficiently drive carrier transitions11,12. As a result, the absorption efficiency under normal incidence is intrinsically limited, posing a critical challenge for direct integration into compact imaging systems. To overcome this limitation, a variety of photonic coupling strategies have been explored. Metallic gratings were introduced to provide the required out-of-plane electric-field component under normal incidence13, while plasmonic resonators14 and metamaterial absorbers15 were subsequently employed to enhance local electromagnetic fields and improve detector responsivity. More recently, metasurfaces have emerged as a versatile platform for tailoring light–matter interactions in MQWs through engineered
Aug 21, 2026 · via nature.com
India appears to be normalising the technical ability to subject political gatherings to searchable biometric surveillance without first having settled, through legislation and judicial oversight, the circumstances in which the state may lawfully do so. While the Delhi Police have continued to deny the use of excessive force and manhandling people involved in the Cockroach Janta Party protests, contrary to protestors’ testimonies, facial recognition equipment was present at the protest site while the demonstrations were on. According to its affidavit to the Supreme Court of India, the police deployed a facial recognition system (including Artificial Intelligence-enabled cameras to scan faces in real-time against a database), a mobile surveillance van and a command and control vehicle, smart spectacles for identifying individuals on the move, and drones and videographers; the van and the spectacles have been tied to private contractors. The police have also not addressed whether actual biometric processing occurred with every individual within the range of cameras. Even if the police discarded the images associated with infructuous checks, that the data may have been accessible to private contractors, whose terms of engagement are unclear, is worrisome. However, no statute governs the use of facial recognition systems; the Digital Personal Data Protection Act, whose data-processing obligations are not yet in force, still makes broad exemptions for state agencies. The Automated Facial Recognition System of the National Crime Records Bureau can be used to identify criminals, unidentified bodies, and so on, and the Criminal Procedure (Identification) Act 2022 expanded the set of records that the police may collect from specified persons. However, these actions are still only allowed vis-à-vis specific groups of people. Irrespective of the constitutional validity of the police’s actions, the chilling effect on potential participants may also curtail the right to protest. The state has to pass a well-established
Aug 21, 2026 · via thehindu.com
Police have made several arrests and recovered a weapon following the latest deployment of live facial recognition cameras in Bradford city centre. West Yorkshire Police used the technology over three days from August 19 to 21, with support from the Bradford City Neighbourhood Policing Team. The cameras helped locate a missing person and identified four individuals wanted for criminal offences, all of whom were arrested. One of those detained, a man wanted for theft, was also arrested on suspicion of possessing an offensive weapon after officers found a knuckle duster. Inspector Justin Adams of the Bradford City Neighbourhood Policing Team said: "The Live Facial Recognition Team have again supported City NPT not only in helping us locate wanted individuals but also in supporting our commitment to protecting vulnerable people. "It has helped officers find a high-risk missing person quickly, ensuring they received the support and safeguarding they needed at the earliest possible opportunity. "The technology also helps us identify those on orders who may cause harm, enabling officers to intervene, engage and take action to keep our communities safe. "Local businesses and traders have broadly welcomed the use of LFR over the last six months, which is now supported by funding from the Knife Crime Concentration Fund." Since February 2026, live facial recognition camera vans have been deployed in Bradford 17 times, resulting in 29 arrests. The system also flagged 21 individuals subject to Sexual Harm Prevention Orders, who were then checked for compliance. Live facial recognition works by comparing real-time camera images to a watchlist of wanted individuals and suspects. Alerts generated by the system are reviewed by trained officers before any action is taken. Images that do not match anyone on the watchlist are deleted within seconds. West Yorkshire Police said the technology is helping to keep communities
Aug 21, 2026 · via thetelegraphandargus.co.uk
Idemia PS face biometrics score high accuracy at scale in latest NIST FRTE Idemia Public Security’s latest facial recognition algorithm has ranked first across the tested gallery sizes in the frontal mugshot-to-mugshot scenario of NIST’s latest FRTE 1:N Identification evaluation. The submission recorded a 1.11 percent FNIR against the 12 million-person gallery, with an FPIR of 0.1 percent. Its ability to maintain low error rates as the enrolled population increased shows the strongest result. Scale provides useful context for differentiating leading biometric algorithms as they converge on controlled image comparisons. Idemia’s (013) face biometerics algorithm delivered an FNIR of 0.05 percent in mugshot identification with databases of 1.6 million and 12 million images, and 0.55 percent on webcam-to-mugshot comparisons, all best in the field. The company’s results remain close to those of the leaders in other controlled or near-frontal scenarios. In visa-to-border comparisons, Idemia scored a 0.13 percent FNIR, third overall but also tops so far in the “by developer” rankings, because NEC’s (010) submission scored better overall than its (011) and (012) algorithms, which have lowest two FNIRs to date. Idemia ranks ninth for visa-to-kiosk identification at 4.74 percent and twenty-sixth when matching profile images against a mugshot gallery with an FNIR of 7.50 percent. The company also finished in the top tier for demographic fairness by FPIR, according to an announcement celebrating its results. The new submission shows a substantial improvement over its algorithm evaluated last year. In visa-to-border identification, FNIR declined from 0.22 percent to 0.13 percent at the same 0.3 percent FPIR operating point. Progress also extends to several other tests, with border-to-border FNIR falling from 2.42 percent to 1.21 percent and mugshot-to-webcam FNIR declining from 0.82 percent to 0.55 percent. The visa-to-kiosk performance improved from 5.16 percent to 4.74 percent. “These results validate our investments
Aug 21, 2026 · via biometricupdate.com
AI Inference — GPT2 Artificial Intelligence and Machine Learning have enabled us to create applications that are almost magical in their abilities. GPT-2 is a powerful language model developed by OpenAI, the makers of ChatGPT, that excels at generating human-quality text. It's a key AI workload because it tests the efficiency and capability of AI systems in handling complex language tasks, which is crucial for many real-world applications. We are measuring the time it takes to generate 100 stories starting with the "Once upon a time, there was a" prompt. AI Inference — Stable Diffusion Stable Diffusion stands out as the second rock star AI workload. This cutting-edge image generation model creates high-quality, detailed visuals from textual descriptions. Its ability to produce realistic images has wide-ranging implications for industries such as art, design, advertising, and media, enabling innovative content creation and enhancing visual storytelling. We are timing how long it takes to generate a single image for the prompt "a photo of an astronaut riding a horse on Mars." AI / Inference — Image Upscaling Topaz Photo AI is a premier tool for AI-driven image processing. It enhances image resolution and quality by intelligently increasing detail and reducing noise. This advanced capability improves the clarity and sharpness of assets, making it a valuable tool for anyone needing high-resolution images. Our test reports how long it takes to upscale a 1.5 megapixel image to 22 megapixels. AI Training — Natural Language Processing Natural Language Processing (NLP) involves training AI models in understanding and generating human language. By applying algorithms and machine learning techniques to analyze and interpret text data, NLP enables tasks like translation, sentiment analysis, and text generation. We are measuring how long it takes to train a BERT language model with a collection of movie critic reviews. AI Training
Aug 21, 2026 · via techpowerup.com
This week, EFF, along with Big Brother Watch, Defend Digital Me, Liberty, Open Rights Group, Race Equality First, Statewatch, and Stopwatch, wrote to Nottinghamshire Police Force in the UK raising concern about the proposed roll-out of live facial recognition technology (LFR), and called for its immediate halt. In particular, the letter highlights six concerns: LFR Is Not "Just Another Tool" Nottinghamshire Police has stated that “facial recognition is just another tool to fight crime.” But LFR used in public spaces is an incredibly intrusive biometric mass surveillance technology that scans the faces of everyone who walks past the camera and takes biometric face prints. This is not just another tool, but a major escalation of surveillance that treats everyone as a suspect by default. People Having "Nothing to Worry About" Does Not Hold to Scrutiny According to Nottinghamshire Police, “if you aren’t entering the city or county to commit crime then you have nothing to worry about.” However, many people have legitimate concerns about the normalisation of invasive technologies. So a public that cannot move around their towns and cities without being subjected to a biometric identity check may be less willing to seek medical care or legal advice, speak with journalists, act in a union, vote, protest, or express their gender, sexual or religious identity. Disproportionate Targeting With LFR We are particularly concerned to learn that Nottinghamshire Police could deploy LFR to tackle low level crimes, such as youth behavior deemed anti-social, as part of Operation View. Reporting suggests that the force already possesses “a watchlist of young people believed to be causing the most problems,” including children as young as 11 years old. It would be highly disproportionate to deploy live facial recognition to tackle this behaviour. Many of these children are reportedly known to the police, and
Aug 21, 2026 · via eff.org
Research from the University of East Anglia shows how our memories change with age and why specific details associated with past events can fade over time. A new study reveals that as people age, they not only remember fewer details from their past - but also struggle more when switching between different types of memories. It points to a shift in how we remember things as we grow old - where vivid and moment-specific recollections gradually give way to broader, more generalised knowledge. Rather than portraying memory decline as a simple loss, the study offers a nuanced picture of aging cognition and sheds light on how memory evolves over a lifetime. The team hope their findings could help guide new approaches to support healthy cognitive aging. Lead researcher Prof Louis Renoult, from UEA's School of Psychology, said: "As we get older, our memories tend to become less detailed and more general, focusing on the overall story rather than specific moments. "This shift is linked to changes in how the brain recalls information, meaning older people often remember the big picture but with fewer vivid details. "We wanted to better understand how young and older adults retrieve autobiographical memories - the personal experiences that form the story of our lives. "These sorts of memories can range from specific one-time events, such as a birthday party, to more general or repeated experiences like commuting to work. "We focused on how flexible people are when asked to shift between these types of memories, and what happens when that flexibility is put under pressure." How the research happened A total of 37 students, representing young minds, and 37 older adults from the community took part in the study. They were asked to recall two kinds of memories in response to cue words such as
Aug 21, 2026 · via news-medical.net
NIST picks 11 contactless fingerprint biometrics providers for certification testing NIST has completed the first phase of its vendor engagements under the Fast Capture (FastCap) program for evaluating contactless fingerprint technologies. Eleven biometrics providers have joined the program under a Cooperative Research and Development Agreement (CRADA) following successful qualification testing. The testing stage included cooperative evaluations of the vendor technologies’ biometric acquisition capabilities, refinement of performance metrics and prototype testing for contactless rapid tenprint scanners. The 11 vendors that satisfied the qualification test criteria are: U.S.-based AA Technology, Gambit ID, which has offices in Canada and the U.S., Idemia, Identy, IDloop, Mentalix, Sciometrics, Synolo, Thales, Tech5 and Veridium. The qualification holds up these biometrics developers as global leaders in contactless fingerprints. The qualification testing was carried out based on NIST Special Publication 500-339, “Specification for Certification Testing of Contactless Fingerprint Acquisition Devices, 1.0,” published in April, 2023. The specification emphasizes the “fundamental departure” that contactless fingerprint biometrics represent from legacy contact-based capture technologies. The steps, procedures and software for the tests, along with the properties to be measured, are defined in NIST SP 500-336. SP 500-336, “Specification for Interoperability Testing of Contactless Fingerprint Acquisition Devices, v1.0,” was published in 2022. The same NIST document explains the creation of standardized test fingers for use in comparing the 2D images created by contact-based fingerprint scanners and the 3D images captured by contactless biometric scans. Properties to be measured (measurands) include image entropy, signal to noise ratio, scale factor computations and NFIQ image quality assessment. The next step is for NIST to review the qualification testing outcomes. Phase Two of the FastCap initiative will involve more vendor engagement as well as government bodies to encourage the adoption of contactless biometrics. The qualification testing model will also be expanded into a framework for ongoing
Aug 21, 2026 · via biometricupdate.com
The unprecedented convergence of AI and Machine Learning in Biology with the life sciences, often described as AI in biology, represents a milestone in scientific progress. AI/ML, once the exclusive tools of computer scientists, is now being incorporated into several scientific disciplines, with biology at the forefront. At the vanguard of a new era of biomedical discovery and innovation, these technologies empower researchers to address an expanding spectrum of scientific questions, ranging from the intricate biochemical mechanisms that govern cellular functions to the urgent challenges in modern healthcare, opening new avenues for understanding the fundamental processes of life. Artificial intelligence is the broader field of developing systems capable of performing tasks generally associated with human intelligence. Machine learning is a subset of AI in which algorithms learn patterns from data and use them to generate predictions, classifications or recommendations. Table of Contents Why Is AI and Machine Learning in Biology Important for Research? Modern biology generates enormous and diverse datasets across the domains of genomics, proteomics, imaging, and clinical records. The need to process this information to extract meaningful patterns and insights often proves to be a formidable challenge, requiring substantial analytical effort by researchers. This is where the unique strengths and capabilities of AI/ML come to the forefront, enabling researchers to perform tasks such as: - Pattern Recognition in Large, Complex Datasets AI and ML algorithms are uniquely capable of detecting subtle patterns and associations that might be challenging or impossible for humans to discern, therefore often guiding the research process. - Predictive Modelling AI/ML can identify trends and patterns that can be used to build predictive models capable of rapidly analyzing complex biological systems and processes, such as disease progression or molecular interactions. These include, but are not limited to, modelling disease processes, predicting drug-target interactions, and identifying
Aug 21, 2026 · via biotecnika.org
Exclusive: Trump DOJ appears to have hired lawyer convicted of hacking election sites to check ‘integrity’ A Florida lawyer who pleaded guilty after illegally accessing government election websites appears to have joined President Donald Trump’s Department of Justice (DOJ) — and taken part in a recent DOJ election monitoring operation in Minnesota. The apparent hiring of David Michael Levin would be among the most troubling example yet of the department’s Civil Rights Division bringing on lawyers — including the acting chief of the voting section — who have taken extreme steps to undermine fair elections, as it rushes to put together a team to carry out Trump’s anti-voting agenda. And Levin’s involvement in election monitoring could offer a hint of the kind of personnel who will staff the operation this fall, when the DOJ has said it will send 1,000 monitors to the polls. Get updates straight to your inbox — for free Join 350,000 readers who rely on our daily and weekly newsletters for the latest in voting, elections and democracy. Levin was charged with three felonies in 2016 after Florida investigators said he used a cyberattack to obtain credentials, then entered restricted portions of a county elections website, and separately accessed the state Division of Elections website. Levin later said he had wanted to look into “the integrity of elections.” The charges were later reduced to two misdemeanors. Levin pleaded guilty, served 20 days in jail and received two years of probation. Now, images and video posted online suggest Levin has joined the DOJ’s Civil Rights Division — the federal office charged with enforcing many of the nation’s voting rights laws and that is now leading the Trump administration’s nationwide grab for sensitive voter data. Democracy Docket initially identified Levin using publicly available facial-recognition reverse image search tools.
Aug 21, 2026 · via democracydocket.com
The Brea Police Department will soon be able to use facial recognition software to identify potential suspects during investigations. On Tuesday, Brea City Council voted unanimously with no discussion to enter into an agreement with the Integrated Law and Justice Agency for Orange County (ILJAOC). This agreement will also give Brea PD access to a system able to analyze faces from surveillance camera footage, a photo supplied from a victim or witness and more. It comes as there’s growing concerns about increased police surveillance throughout Orange County, with some residents criticizing such systems. [Read: OC Cities Look to Beef Up Surveillance Amid Privacy Concerns] Brea is the 13th law enforcement agency in Orange County to enter into this agreement. “The PD regularly receives Ring video, photos, and CCTV footage from victims and witnesses related to crimes. Many of these have clear images of the suspect, but we still don’t know who they are,” wrote Professional Standards Lieutenant Chris Haddad in an email to Voice of OC. “This could give the investigator an additional lead to investigate if the system provides a potential match,” he wrote. The system works by analyzing images submitted by an authorized investigator. The software then creates a mathematical representation of facial characteristics seen in the photos and matches them with photographs in a jail booking database. The software produces candidate matches that a trained human investigator must evaluate using other evidence. Haddad explained how the system will only compare photos of unknown persons against booking photos of known people. “This is simply a tool that may create leads if the police department has a clear image of a suspect and that suspect has been booked into county jail at some point and has a booking photo in the system,” he wrote. According to the system’s policy,
Aug 21, 2026 · via voiceofoc.org
A Complete Guide to Flock Safety Cameras If you live in America, chances are you’ve driven past a Flock camera at some point over the past few years. Flock has become nearly ubiquitous thanks to its use by law enforcement, HOAs, and private property owners, with a footprint in over 5,000 communities across 49 states. Flock’s technology and hardware, including automated license plate recognition and gunfire locator systems, mostly flew under the radar since its inception in 2017. But in recent years, the company has seen increasing scrutiny, criticism, and litigation from private citizens, local governments, and civil liberties groups over concerns regarding their potential invasion of privacy and misuse. What are Flock cameras, and should you be concerned about their use in your community? What are Flock Cameras? Flock Safety, the company that builds and operates these cameras, says that they seek to deter crime, respond to emergencies, and investigate safety incidents. Its main products are automated license plate readers, or ALPRs, though they also offer traditional security cameras, mobile security trailers, drones, and security software. ALPRs capture identifying details of passing cars, including the license plate, make, model, color, and other visible characteristics. The cameras—which are located on public roads, in parking lots, and in front of private homes—use AI technology to analyze what it captures and upload that data to the company’s national surveillance platform. They do not use facial recognition technology, though images of pedestrians or bicyclists may be captured in the recordings. Flock is most notably used by law enforcement agencies, but the cameras are also heavily marketed to HOAs, schools, and retail businesses, which can then share their camera data to local police. This helps the company create an expansive surveillance network, with the stated goal of aiding criminal investigations. Flock says it captures
Aug 21, 2026 · via realtor.com
Brazil's National Data Protection Authority (ANPD) has ordered the school system in the southern state of Paraná to immediately stop collecting biometric data of children and adolescents for attendance using facial recognition software, with further coverage provided by Biometric Update.The ANPD cited a failure by the Paraná State Department of Education to demonstrate an adequate legal basis for processing sensitive biometric data, nor sufficient guarantees of security and governance. The pilot project, which involved approximately one million students across 2,136 institutions, utilized facial recognition software from Innovatrics, subcontracted by Valid, and deployed through the Escola Paraná Biometria app. Teachers photographed classes, and images were sent to cloud servers for facial extraction and comparison.The teachers' union questioned the necessity of biometric data collection, stating traditional attendance methods are sufficient. The ANPD agreed, deeming facial recognition unnecessary and disproportionate, noting less invasive alternatives. Concerns were also raised about inadequate controls over image sharing, privacy, and data retention, potentially storing 14 years of biometric data per student. The ANPD has given Paraná 10 days to confirm the cessation of data processing across all involved systems and companies.Source: Biometric Update Get daily email updates SC Media's daily must-read of the most current and pressing daily news You can skip this ad in 5 seconds
Aug 20, 2026 · via scworld.com
AI and AV are the New Drivers of the Ever-Evolving Public Transit Future The history of public transportation is one of constant evolution and reinvention. Since the origin of the American Public Transportation Association (APTA) in 1882, transit providers have been at the center of continuous technology and institutional change. Through each ensuing decade, transit systems have been ever evolving. Over the past ten years, technologies enabling mobility-on-demand (TNCs, bike-share, micro transit) have become part of the urban mobility landscape. Today, automated vehicles (AV), artificial intelligence (AI), and machine learning (ML) are at the cutting edge of another wave of technology-driven innovation. Transit agencies are eager to better understand the potential for AI so APTA has been working to provide resources and opportunities for information sharing. The recent APTAtech Conference in St. Louis (August 9-12, 2026) provided the opportunity for a deep-dive into the various ways transit agencies are putting Artificial Intelligence to work today to boost efficiency, service delivery, security, and the overall rider experience. Prior to that, in May 2026, APTA released Artificial Intelligence (AI) and Machine Learning (ML) in Public Transit: A Primer, providing public transit agencies with a comprehensive resource for understanding, evaluating, and deploying AI and ML tools across their operations. Findings were based on a survey of transit agencies and staff interviews to capture current and planned AI applications. Real-world examples documented in the Primer include: - Metropolitan Transportation Authority (NY) increased maintenance productivity by 75 percent and decreased material costs by 24 percent in a test fleet, using an AI-powered predictive maintenance system for its bus fleet. - AC Transit (CA) used AI image recognition for bus lane enforcement, increasing violation citations from 22 to 787 over a comparable two-month period following implementation. - Riverside Transit Agency (CA) piloted a disruption management tool
Aug 20, 2026 · via enotrans.org
Live facial recognition technology will be deployed in Aylesbury town centre next week as part of efforts to identify wanted suspects and deter crime. Thames Valley Police has announced that its specialist Live Facial Recognition (LFR) team will be operating in Market Square on Tuesday, August 25, alongside local officers. The force said the technology will be used to help identify known suspects, prevent offending and keep communities safe. Police routinely publicise planned deployments in advance, despite acknowledging that some individuals may seek to avoid the area as a result. Officers will be on hand throughout the day to answer questions from members of the public and explain how the technology works. Live Facial Recognition uses cameras to analyse facial features and compare them against a pre-determined watchlist of individuals wanted by police or sought in connection with investigations. The force also uses Retrospective Facial Recognition, which compares still images, such as those captured on CCTV, against the Police National Database to assist in identifying unknown suspects. Thames Valley Police said it recognises the need to balance public safety with privacy concerns and considers the legal and ethical implications whenever facial recognition technology is used. Residents who would like to learn more about the system have been encouraged to speak to officers during the deployment in Market Square. Share
Aug 20, 2026 · via bucksfreepress.co.uk