SCTracker: Multi-object tracking with shape and confidence constraints
This work addresses tracking errors in multi-object scenarios, but it is incremental as it builds on existing detection-based methods with specific enhancements.
The paper tackled the problem of wrong target association in detection-based multi-object tracking due to overlapping and low-confidence detections by proposing SCTracker, which uses shape constraints and confidence-based updates, resulting in improved tracking performance on the MOT 17 dataset.
Detection-based tracking is one of the main methods of multi-object tracking. It can obtain good tracking results when using excellent detectors but it may associate wrong targets when facing overlapping and low-confidence detections. To address this issue, this paper proposes a multi-object tracker based on shape constraint and confidence named SCTracker. In the data association stage, an Intersection of Union distance with shape constraints is applied to calculate the cost matrix between tracks and detections, which can effectively avoid the track tracking to the wrong target with the similar position but inconsistent shape, so as to improve the accuracy of data association. Additionally, the Kalman Filter based on the detection confidence is used to update the motion state to improve the tracking performance when the detection has low confidence. Experimental results on MOT 17 dataset show that the proposed method can effectively improve the tracking performance of multi-object tracking.