PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Robust Tracking-by-Detection using a Detector Confidence Particle Filter
Michael D. Breitenstein, Fabian Reichlin, Bastian Leibe, Esther Koller-Meier and Luc Van Gool
In: IEEE Int. Conference on Computer Vision (ICCV'09), Tokyo(2009).


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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:5491
Deposited By:Michael Breitenstein
Deposited On:03 December 2009