Online multi-person tracking using Integral Channel Features

Abstract

Online multi-person tracking benefits from using an online learned appearance model to associate detections to tracks and further to close gaps in detections. Since Integral Channel Features (ICF) are popular for fast pedestrian detection, we propose an online appearance model that is using the same features without recalculation. The proposed method uses online Multiple-Instance Learning (MIL) to incrementally train an appearance model for each person discriminating against its surrounding. We show that a low number of discriminatingly selected Integral Channel Features are sufficient to achieve state-of-the-art results on the MOT2015 and MOT2016 benchmark.