The Community for Technology Leaders
Green Image
Issue No. 12 - December (2011 vol. 33)
ISSN: 0162-8828
pp: 2477-2491
Tinne De Laet , Katholieke Universiteit Leuven, Leuven
Herman Bruyninckx , Katholieke Universiteit Leuven, Leuven
Joris De Schutter , Katholieke Universiteit Leuven, Leuven
This paper proposes a novel online two-level multitarget tracking and detection (MTTD) algorithm. The algorithm focuses on multitarget detection and tracking for the case of multiple measurements per target and for an unknown and varying number of targets. Information is continuously exchanged in both directions between the two levels. Using the high level target position and shape information, the low level clusters the measurements. Furthermore, the low level features automatic relevance detection (ARD), as it automatically determines the optimal number of clusters from the measurements taking into account the expected target shapes. The high level's data association allows for a varying number of targets. A joint probabilistic data association algorithm looks for associations between clusters of measurements and targets. These associations are used to update the target trackers and the target shapes with the individual measurements. No information is lost in the two-level approach since the measurement information is not summarized into features. The target trackers are based on an underlying motion model, while the high level is supplemented with a filter estimating the number of targets. The algorithm is verified using both simulations and experiments using two sensor modalities, video and laser scanner, for detection and tracking of people and ants.
Multitarget tracking, data association, detection, laser range scanner, video, Bayesian networks, Kalman filter, particle filter.

J. De Schutter, H. Bruyninckx and T. De Laet, "Shape-Based Online Multitarget Tracking and Detection for Targets Causing Multiple Measurements: Variational Bayesian Clustering and Lossless Data Association," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 2477-2491, 2011.
89 ms
(Ver 3.3 (11022016))