DIET: Dynamic Integration of Extended Tracklets for Tracking Multiple Persons

While online approaches for multi-person tracking are still hard to deal with occlusions, many tracking methods use a global data association approach to find person trajectories on a long segment of consecutive frames. Such offline approaches often cause long output latencies, which are unsuitable for camera-based surveillance systems. Based on an observation that the current position of an object should be estimated from both its past and future positions that are observed in a short period of time, we propose a novel object tracking method based on a dynamic programming framework. This method iteratively associates and integrates track lets obtained by visual tracking which are observed just before and after occlusions. We always produce outputs at a constant short delay time, and requires only the standard HOG detector for high performance. Our tracking method can run at 10-60 fps on a single CPU core, and attains the state-of-the-art performance for the Town Center dataset.