Markovian Tracking-by-Detection from a Single, Uncalibrated Camera

We present an algorithm for multi-person tracking-bydetection in a particle filtering framework. To address the unreliability of current state-of-the-art object detectors, our algorithm tightly couples object detection, classification, and tracking components. Instead of relying only on the final, sparse output from a detector, we additionally employ its continuous intermediate output to impart our approach with more flexibility to handle difficult situations. The resulting algorithm robustly tracks a variable number of dynamically moving persons in complex scenes with occlusions. The approach does not rely on background modeling and is based only on 2D information from a single camera, not requiring any camera or ground plane calibration. We evaluate the algorithm on the PETS’09 tracking dataset and discuss the importance of the different algorithm components to robustly handle difficult situations.

• Type: Conference
• Year: 2009
• Where: Proc. Int. IEEE CVPR Workshop on Performance Evaluation of Tracking and Surveillance
• Authors: Michael D. Breitenstein, Fabian Reichlin, Bastian Leibe, Esther Koller-Meier, Luc Van Gool
• Keywords: Multi-Object Tracking, Online Learning, Particle Filtering, PETS 2009, Tracking-by-detection