To track already detected objects in a video, you need a method that maintains consistent identities for objects across frames. This process, called object tracking, relies on associating detections from consecutive frames and updating their positions. The key steps involve initializing tracks for detected objects, predicting their future locations (using motion models), and matching new detections to existing tracks. This ensures each object retains a unique ID even as it moves, occludes, or temporarily leaves the frame. For example, if a car is detected in frame 1, the tracker assigns it an ID and updates its position in frame 2 by linking the new detection to the same ID, even if the car’s appearance changes slightly.
Common algorithms for tracking include the Kalman Filter and the Hungarian algorithm. The Kalman Filter predicts an object’s next position based on its current velocity and motion patterns, which helps handle temporary occlusions. The Hungarian algorithm matches detected objects in the current frame to existing tracks by solving a cost matrix, where lower costs indicate stronger matches. For instance, if two cars are detected in adjacent frames, the algorithm calculates the distance between their bounding boxes and assigns the closest matches. More advanced methods like SORT (Simple Online and Realtime Tracking) combine these techniques, using Kalman Filters for prediction and the Hungarian algorithm for data association. Deep learning-based trackers, such as DeepSORT, add appearance descriptors (e.g., neural network embeddings) to improve matching accuracy when objects look similar.
To implement tracking, start with a detection framework like YOLO or Faster R-CNN to generate bounding boxes for each frame. Then, use a tracking library like OpenCV (with built-in trackers such as KCF or CSRT) or a dedicated toolkit like the tracker module in Python’s Dlib library. For multi-object scenarios, integrate SORT or DeepSORT, which are designed to handle ID assignments and occlusions. For example, using DeepSORT, you’d first run a detector on each frame, extract appearance features from bounding boxes, and then match them to existing tracks using both motion and feature similarity. Challenges include handling rapid motion (which can break Kalman predictions) and computational efficiency—optimizations like limiting feature extraction to regions of interest can help. Testing with public datasets like MOTChallenge can validate performance metrics like IDF1 (identification accuracy) and MOTA (multi-object tracking accuracy).