Image difference threshold strategies and shadow detection
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Automatically tracking and analyzing the behavior of live insect colonies
Proceedings of the fifth international conference on Autonomous agents
Tracking People in a Railway Station During Rush-Hour
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Multitarget Tracking with Split and Merged Measurements
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Target Tracking - Linking Identities using Bayesian Network Inference
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and inferring transportation routines
Artificial Intelligence
Using observations to recognize the behavior of interacting multi-agent systems
Using observations to recognize the behavior of interacting multi-agent systems
People tracking with anonymous and ID-sensors using Rao-Blackwellised particle filters
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Augmenting Live Broadcast Sports with 3D Tracking Information
IEEE MultiMedia
Taking mobile multi-object tracking to the next level: people, unknown objects, and carried items
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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We describe and evaluate a greedy detection-based algorithm for tracking a variable number of dynamic targets online. The algorithm leverages the well-known iterative closest point (ICP) algorithm for aligning target models with target detections. The approach differs from trackers that seek globally optimal solutions because it treats the problem as a set of individual tracking problems. The method works for multiple targets by sequentially matching models to detections, and then removing detections from further consideration once models have been matched to them. This allows targets to pass close to one another with reduced risks of tracking failure due to “hijacking,'' or track merging. There has been significant previous work in this area, but we believe our approach addresses a number of tracking problems simultaneously that have only been addressed separately before. The algorithm is evaluated using four to eight laser range finders in three settings: quantitatively for a basketball game with 10 people and a 25-person social behavior experiment, and qualitatively for a full-scale soccer game. We also provide qualitative results using video to track ants in a captive habitat. During all the experiments, agents enter and leave the scene, so the number of targets to track varies with time. With eight laser range finders running, the system can locate and track targets at sensor frame rate 37.5 Hz on commodity computing hardware. Our evaluation shows that the tracking system correctly detects each track over 98% of the time. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.