Online Visual Multi-Object Tracking via Labeled Random Finite Set Filtering
This work addresses the problem of robust multi-object tracking in videos for applications like surveillance and autonomous driving, offering an incremental improvement in handling occlusions and mis-detections.
The paper tackles online visual multi-object tracking by proposing a Bayesian filter that integrates state estimation, track management, and occlusion handling into a single recursion, achieving improved performance compared to state-of-the-art algorithms on benchmark datasets.
This paper proposes an online visual multi-object tracking algorithm using a top-down Bayesian formulation that seamlessly integrates state estimation, track management, clutter rejection, occlusion and mis-detection handling into a single recursion. This is achieved by modeling the multi-object state as labeled random finite set and using the Bayes recursion to propagate the multi-object filtering density forward in time. The proposed filter updates tracks with detections but switches to image data when mis-detection occurs, thereby exploiting the efficiency of detection data and the accuracy of image data. Furthermore the labeled random finite set framework enables the incorporation of prior knowledge that mis-detections of long tracks which occur in the middle of the scene are likely to be due to occlusions. Such prior knowledge can be exploited to improve occlusion handling, especially long occlusions that can lead to premature track termination in on-line multi-object tracking. Tracking performance are compared to state-of-the-art algorithms on well-known benchmark video datasets.