Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering
This addresses tracking failures in video analysis for applications like surveillance, but it is incremental as it builds on existing methods.
The paper tackles temporal errors in multi-object tracking, such as drift and ID-switching during occlusions or noisy detections, by proposing historical appearance matching and scene adaptive detection filtering, resulting in improved identity consistency.
In this paper, we propose the methods to handle temporal errors during multi-object tracking. Temporal error occurs when objects are occluded or noisy detections appear near the object. In those situations, tracking may fail and various errors like drift or ID-switching occur. It is hard to overcome temporal errors only by using motion and shape information. So, we propose the historical appearance matching method and joint-input siamese network which was trained by 2-step process. It can prevent tracking failures although objects are temporally occluded or last matching information is unreliable. We also provide useful technique to remove noisy detections effectively according to scene condition. Tracking performance, especially identity consistency, is highly improved by attaching our methods.