SCTracker: Multi-object tracking with shape and confidence constraints
Huan Mao Yulin Chen Zongtan Li Feng Chen Pingping Chen
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.