Pedestrian tracking by associating tracklets using detection residuals

Abstract

Due to increased interest in visual surveillance, various multiple object tracking methods have been recently proposed and applied to pedestrian tracking. However in presence of intensive inter-object occlusion and sensor gaps, most of these methods result in tracking failures. We present a two-stage multi-object tracking approach to robustly track pedestrians in such complex scenarios. We first generate high confidence partial track segments (tracklets) using a robust pedestrian detector and then associate the tracklets in a global optimization framework. Unlike the existing two-stage tracking methods, our method uses the unassociated low confidence detections (residuals) between the tracklets, which improves the tracking performance. We evaluate our method on the CAVIAR dataset and show that our method performs better than state-of-the-art methods.