  Computer vision and machine learning have brought revolutionary shifts in
perception for researchers, scientists, and the general populace. Once thought
to 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 of
computer vision remains untapped. This paper explores computer vision's
potential in security and surveillance, presenting a novel approach to track
motion in videos. By categorizing motion into Arcs, Lanes,
Converging/Diverging, and Random/Block motions using Motion Information Images
and Blockwise dominant motion data, the paper examines different optical flow
techniques, CNN models, and machine learning models. Successfully achieving its
objectives with promising accuracy, the results can train anomaly-detection
models, provide behavioral insights based on motion, and enhance scene
comprehension.
