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 unasso- ciated 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.
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