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Paper TuA204.3

Luber, Matthias (University of Freiburg), Tipaldi, Gian Diego (University of Freiburg), Arras, Kai Oliver (University of Freiburg)

Better Models for People Tracking

Scheduled for presentation during the Regular Sessions "Human Detection and Tracking I" (TuA204), Tuesday, May 10, 2011, 10:35−10:50, 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 April 2, 2020

Keywords Human detection & tracking, Sensor Fusion

Abstract

People tracking is a key component for robots operating in populated environments. Previous works have employed different filtering and data association techniques for this purpose that typically rely on a set of generic assumptions on target behavior and detector characteristics. In this paper, we focus on these assumption rather than the tracking approach itself and show that with informed models, people tracking can be made substantially more accurate at no additional cost. Concretely, we present better, human-specific models for the occurrence of new tracks, false alarms, track occlusions, and track deletions. In the experiments with a large-scale outdoor data set collected with a laser range finder, the models and combinations thereof are experimentally compared using a multi-hypothesis baseline tracker and the CLEAR MOT metrics. The results show how some models selectively improve tracking performance at the expense of other measures. The final combination is then able to resolve the trade-offs, leading to a reduction of data association errors by more than a factor of two at the same cost.

 

 

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