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Computer vision and machine learning have brought revolutionary shifts inperception for researchers, scientists, and the general populace. Once thoughtto be unattainable, these technologies have achieved the seemingly impossible.Their exceptional applications in diverse fields like security, agriculture,and education are a testament to their impact. However, the full potential ofcomputer vision remains untapped. This paper explores computer vision'spotential in security and surveillance, presenting a novel approach to trackmotion in videos. By categorizing motion into Arcs, Lanes,Converging/Diverging, and Random/Block motions using Motion Information Imagesand Blockwise dominant motion data, the paper examines different optical flowtechniques, CNN models, and machine learning models. Successfully achieving itsobjectives with promising accuracy, the results can train anomaly-detectionmodels, provide behavioral insights based on motion, and enhance scenecomprehension.

Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning