POI: Multiple Object Tracking with High Performance Detection and Appearance Feature
This work addresses tracking accuracy for computer vision applications, but it is incremental as it builds on existing detection and feature methods.
The paper tackles the problem of multiple object tracking by emphasizing high-performance detection and deep learning-based appearance features, showing that these lead to significantly better results in both online and offline settings.
Detection and learning based appearance feature play the central role in data association based multiple object tracking (MOT), but most recent MOT works usually ignore them and only focus on the hand-crafted feature and association algorithms. In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting. We make our detection and appearance feature publicly available. In the following part, we first summarize the detection and appearance feature, and then introduce our tracker named Person of Interest (POI), which has both online and offline version.