Tracking by Detection

Tracking-by-detection is a dominant paradigm in multi-object tracking (MOT), aiming to accurately identify and track objects across video sequences by associating detections from object detectors. Current research emphasizes improving robustness and efficiency, focusing on novel association methods that leverage diverse cues like bounding boxes, segmentation masks, and spatiotemporal information, often implemented using transformer networks, graph neural networks, or Kalman filter variants. These advancements are crucial for applications like autonomous driving, surveillance, and biological image analysis, where reliable and real-time object tracking is essential for safe and efficient operation.