Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking groups of people with a multi-model hypothesis tracker
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Tracking multiple speakers with probabilistic data association filters
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Multiple and variable target visual tracking for video-surveillance applications
Pattern Recognition Letters
Humans tracking in the complicated background by multi-cue integration
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 3
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
International Journal of Computer Vision
Robust hierarchical multiple hypothesis tracker for multiple-object tracking
Expert Systems with Applications: An International Journal
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Tracking with a mixed continuous-discrete Conditional Random Field
Computer Vision and Image Understanding
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Data association is a fundamental problem when tracking large numbers of moving targets. Most commonly employed methods of data association such as the JPDA estimator are combinatorial and therefore do not scale well to large numbers of targets. However, in many cases large numbers of targets form natural groups which can be efficiently tracked. We describe a method for defining groups based on the position and velocity of targets. This definition introduces a natural set of merging and splitting rules that are embedded into a Kalman filtering framework for tracking multiple groups. In cases where groups of different velocities cross, a general methodology for matching measurements to groups is introduced. This algorithm is based on a modified version of the PDA estimator. It is well suited to handle a high number of measurements and extends naturally to additional grouping constraints such as color or shape.