Integrated Object Detection and Tracking with Tracklet-Conditioned Detection

TL;DR

This paper introduces a method that tightly integrates object detection and tracking by conditioning detection on prior tracklets, resulting in more coherent trajectories and improved accuracy in video analysis.

Contribution

The proposed approach uniquely conditions detection on existing tracklets, enhancing the synergy between detection and tracking for more stable and accurate video object analysis.

Findings

Achieves state-of-the-art detection and tracking accuracy.

Produces smoother and more stable object trajectories.

Significantly improves tracking stability over previous methods.

Abstract

Accurate detection and tracking of objects is vital for effective video understanding. In previous work, the two tasks have been combined in a way that tracking is based heavily on detection, but the detection benefits marginally from the tracking. To increase synergy, we propose to more tightly integrate the tasks by conditioning the object detection in the current frame on tracklets computed in prior frames. With this approach, the object detection results not only have high detection responses, but also improved coherence with the existing tracklets. This greater coherence leads to estimated object trajectories that are smoother and more stable than the jittered paths obtained without tracklet-conditioned detection. Over extensive experiments, this approach is shown to achieve state-of-the-art performance in terms of both detection and tracking accuracy, as well as noticeable improvements in tracking stability.