ICRA 2011 Paper Abstract


Paper TuP104.3

Spinello, Luciano (Albert-Ludwigs-Universitšt Freiburg), Luber, Matthias (University of Freiburg), Arras, Kai Oliver (University of Freiburg)

Tracking People in 3D Using a Bottom-Up Top-Down Detector

Scheduled for presentation during the Regular Sessions "Human Detection and Tracking II" (TuP104), Tuesday, May 10, 2011, 14:10−14:25, Room 3E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 29, 2020

Keywords Human detection & tracking, Range Sensing, Recognition


People detection and tracking is a key component for robots and autonomous vehicles in human environments. While prior work mainly employed image or 2D range data for this task, in this paper, we address the problem using 3D range data. In our approach, a top-down classifier selects hypotheses from a bottom-up detector, both based on sets of boosted features. The bottom-up detector learns a layered person model from a bank of specialized classifiers for different height levels of people that collectively vote into a continuous space. Modes in this space represent detection candidates that each postulate a segmentation hypothesis of the data. In the top-down step, the candidates are classified using features that are computed in voxels of a boosted volume tessellation. We learn the optimal volume tessellation as it enables the method to stably deal with sparsely sampled and articulated objects. We then combine the detector with tracking in 3D for which we take a multi-target multi-hypothesis tracking approach. The method neither needs a ground plane assumption nor relies on background learning. The results from experiments in populated urban environments demonstrate 3D tracking and highly robust people detection up to 20 m with equal error rates of at least 93%.



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