Occlusion Handling in Tracking Multiple People Using RNN

In tracking-by-detection of multiple targets in video sequences, ID-switch is an undesirable error due to long (short) occlusion among targets. In this paper, we propose an occlusion handling method based on Recurrent Neural Network (RNN) to remedy this issue. The method reconstructs missed detection boxes in order to preserve the ID number of targets after occlusion by predicting the detections in next frames. The prediction is accomplished by learning the motion of targets using a novel RNN. Applying this technique on tracking results of several state-of-the-arts shows that their ID-switch error is reduced.