This whitepaper provides an in-depth analysis of object tracking in video, highlighting why tracking is more complex than single-frame object detection. It explains challenges such as occlusion, motion blur, scale variation, and identity switching in crowded scenes. The paper reviews traditional tracking methods and contrasts them with modern deep learning approaches, including CNN-based trackers and CNN–LSTM hybrids. It categorizes tracking algorithms by online versus offline processing and single versus multi-object tracking. Applications such as surveillance, traffic monitoring, robotics, and safety systems demonstrate how advanced tracking models improve accuracy and robustness.

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