PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Markovian Tracking-by-Detection from a Single, Uncalibrated Camera
Michael D. Breitenstein, Fabian Reichlin, Bastian Leibe, Esther Koller-Meier and Luc Van Gool
In: Proc. Int. IEEE CVPR Workshop on Performance Evaluation of Tracking and Surveillance (PETS'09), Miami(2009).


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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:5494
Deposited By:Michael Breitenstein
Deposited On:03 December 2009