To track already detected objects in a video, first use object detection models like YOLO or SSD to identify objects in each frame. Apply tracking algorithms, such as SORT (Simple Online and Realtime Tracking) or DeepSORT, to maintain object identity over consecutive frames.
For optical flow-based tracking, use methods like Lucas-Kanade in OpenCV to track motion at the pixel level. Kalman filters can predict object positions in cases of partial occlusion.
Test the tracking system on annotated video datasets like MOT (Multiple Object Tracking) to ensure accuracy. Tune hyperparameters to handle challenges like fast motion or object overlap effectively.