Abstract Inter-camera person re-identification (re-ID), is the process of identifying people in a surveillance system from various camera perspectives. It involves confirming person identification throughout several cameras and navigating limitations like transferring lighting, converting views, and occlusions all important for safety and monitoring applications. Managing differences in camera angles, occlusions, and illumination may be difficult. These elements may cause mismatches among people, which could decrease the re-ID system’s basic efficacy. This research proposed a novel technique to enhance Single Shot Unsupervised Domain Adaptation for Inter-camera Person Re-ID to address this problem, proposed work consists of Preprocessing, and Classification. Initially the preprocessing is applied via Augmentation the use of Cycle GAN, Noise reduction the use of Median Filter, and Enhance Image contrast using Histogram Equalization (HE). Using those preprocessed data, the Siamese Network is trained under the Classification stage. To further enhance the procedure inside the Siamese Network, utilize Conv50 and Conv152. The Python platform is used to develop the suggested model, and performance metrices are used to evaluate the model’s effectiveness. Similar content being viewed by others Introduction Inter-camera person re-ID is a computer vision issue, that refers to the processes of re-identifying humans across different cameras used in surveillance systems1. Overcoming adjustments in point of view, lighting, image resolution, and occlusions is vital. In this regard, the purpose is to suit one particular character identified in a given surveillance community camera frame to the equal individual performing in any other camera frame with utmost accuracy2,3. Hence, state-of-the-art techniques like deep neural networks, metric learning, and feature extraction are usually used to solve this problem. Inter-camera person re-ID allows people to be accompanied in several locations and makes surveillance systems powerful and efficient inside the surveillance of public places and public safety4. It has vast programs, from protection and public