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