Robust online multiple object tracking based on the confidence-based relative motion network and correlation filter
Se-Hoon Park, Kyuewang Lee, Kuk‐Jin Yoon · 2016
When tracking multiple objects in an image sequence, various difficulties such as occlusion, mis-detection, false detection, and abrupt camera motion often occur together. Nevertheless, previous methods on multi-object tracking generally focus on only one or two of them. For that reason, the previous methods could not handle various problematic situations, where multiple difficulties occur simultaneously. To overcome this limitation, we propose a unified framework that can handle such difficulties concurrently, where we effectively combine the confidence-based two-step data association and relative motion network with correlation filtering. We show that the proposed unified framework yields noticeable performance enhancement under various difficulties.