Combining patch matching and detection for robust pedestrian tracking in monocular calibrated cameras

• We found a way of using the information of a pedestrian detector in a WVMF framework.
• Motion prediction and detections can help the tracker recover from failure.
• We compare different methods for pedestrian tracking in publicly available videos.
• Our method outperforms some of the current state-of-the-art trackers.

This paper presents a new approach for tracking multiple people in monocular calibrated cameras combining patch matching and pedestrian detection. Initially, background removal and pedestrian detection are used in conjunction with the vertical standing hypothesis to initialize the targets with multiples patches. In the tracking step, each patch related to a given target is matched individually across frames, and their translation vectors are combined robustly with pedestrian detection results in the world coordinate frame using weighted vector median filters. Additionally, the algorithm uses the camera parameters to both estimate the person scale in a straightforward manner and to limit the search region used to track each fragment. Our experimental results indicate that our tracker can deal with occlusions and video sequences with strong appearance variations, presenting results comparable to or better than existing state-of-the-art algorithms.