Multi-object tracking via high accuracy optical flowand finite set statistics

In this work we present a novel method for tracking an un known number of objects with a single camera system in real time. The proposed algorithm is based on high-accuracy optical flow and finite set statistics. In this framework the target state is treated as a random vector and the number of possible objects as a random number, which has to be estimated correctly. We are able to deal with false alarms, clutter and object spawning. Since possible objects can appear or disappear in the scene we propose a probability model for these events, in order to obtain stable results in the case of missing detections. Additionally, we show how track labeling, based on color and state information, can improve the results. Since the method partly relies on color information, it can handle partial occlusion and is invariant to rotation and scaling. We verify the theoretical results on various scenes.