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.

计算机视觉和机器学习在研究员、科学家和大众的感知力方面产生了革命性的转变。本文探讨了计算机视觉在安全监控方面的潜力，并提出了一种新颖的视频运动追踪方法，通过使用动态信息图像和块状主导运动数据将运动分类为弧形、车道、汇合 / 分散和随机 / 块状运动，并研究了不同的光流技术、CNN 模型和机器学习模型。通过取得有希望的准确性达到其目标，结果可以训练异常检测模型，基于运动提供行为洞察，并增强场景理解。