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ICCV
2009
IEEE

Robust Tracking-by-Detection using a Detector Confidence Particle Filter

10 years 5 months ago
Robust Tracking-by-Detection using a Detector Confidence Particle Filter
We propose a novel approach for multi-person trackingby- detection in a particle filtering framework. In addition to final high-confidence detections, our algorithm uses the continuous confidence of pedestrian detectors and online trained, instance-specific classifiers as a graded observation model. Thus, generic object category knowledge is complemented by instance-specific information. A main contribution of this paper is the exploration of how these unreliable information sources can be used for multi-person tracking. The resulting algorithm robustly tracks a large number of dynamically moving persons in complex scenes with occlusions, does not rely on background modeling, and operates entirely in 2D (requiring no camera or ground plane calibration). Our Markovian approach relies only on information from the past and is suitable for online applications. We evaluate the performance on a variety of datasets and show that it improves upon state-of-the-art methods.
Michael D. Breitenstein, Fabian Reichlin, Bastian
Added 13 Jul 2009
Updated 10 Jan 2010
Type Conference
Year 2009
Where ICCV
Authors Michael D. Breitenstein, Fabian Reichlin, Bastian Leibe, Esther Koller-Meier, Luc Van Gool
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