Title :
Shape-Based Online Multitarget Tracking and Detection for Targets Causing Multiple Measurements: Variational Bayesian Clustering and Lossless Data Association
Author :
De Laet, Tinne ; Bruyninckx, Herman ; De Schutter, Joris
Author_Institution :
Dept. of Mech. Eng., Katholieke Univ. Leuven, Heverlee, Belgium
Abstract :
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.
Keywords :
Bayes methods; object tracking; pattern clustering; sensor fusion; shape recognition; target tracking; ARD; MTTD; automatic relevance detection; lossless data association; multitarget tracking and detection; probabilistic data association algorithm; variational Bayesian clustering; Bayesian methods; Clustering algorithms; Loss measurement; Radar tracking; Target tracking; Time measurement; Bayesian networks; Kalman filter; Multitarget tracking; data association; detection; laser range scanner; particle filter.; video;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Conference_Location :
5/12/2011 12:00:00 AM
DOI :
10.1109/TPAMI.2011.83