Online Multi-Person Tracking-by-Detection from a Single, Uncalibrated Camera

In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex scenes using a monocular, potentially moving, uncalibrated camera. We propose a novel approach for multi-person tracking-bydetection 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. The main contribution of this paper is to explore how these unreliable information sources can be used for robust multi-person tracking. The algorithm detects and tracks a large number of dynamically moving persons in complex scenes with occlusions, does not rely on background modeling, requires no camera or ground plane calibration, and only makes use of information from the past. Hence, it imposes very few restri...

• Type: Journal
• Year: 2010
• Where: IEEE Transactions on Pattern Analysis and Machine Intelligence
• Authors: Michael D. Breitenstein, Fabian Reichlin, Bastian Leibe, Esther Koller-Meier, Luc Van Gool
• Keywords: Detector Confidence, Detector Confidence Particle Filter, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2010, Multi-Object Tracking, Online Learning, Particle Filtering, Pedestrian Detection, Sequential Monte Carlo Estimation, Sports Analysis, Surveillance, Tracking-by-detection, Traffic Safety