Adam Feldman
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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.
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Using Observations to Recognize the Behavior of Interacting Multi-Agent Systems
Ph.D. Thesis, Georgia Institute of Technology (2008)
Real-time tracking of multiple targets using multiple laser scanners
Summer Adams
Maria Hybinette
Tucker Balch
Proceedings of Measuring Behavior, Noldus, Maastricht, The Netherlands (2008), pp. 136-137
A tracker for multiple dynamic targets using multiple sensors
Summer Adams
Maria Hybinette
Tucker Balch
IEEE International Conference on Robotics and Automation (2007), pp. 3140-3141
How A.I. and multi-robot systems research will accelerate our understanding of social animal behavior
Tucker Balch
Frank Dellaert
Andrew Guillory
Charles Isbell
Zia Khan
Andrew Stein
Hank Wilde
Proceedings of the IEEE, vol. 94 (2006), pp. 1445-1463
Modeling Honey Bee Behavior for Recognition Using Human Trainable Models
Tucker Balch
Modeling Other Agents from Observations (Workshop at AAMAS), New York, USA (2004), pp. 17-24
Assessment of an RFID System for Animal Tracking
Tucker Balch
Wesley Wilson
Georgia Institute of Technology, Georgia Institute of Technology, Atlanta, Georgia, USA (2004)
Representing honey bee behavior for recognition using human trainable models
Automatic Identification of Bee Movement
Tucker Balch
2nd International Workshop on the Mathematics and Algorithms of Social Insects, Atlanta, Georgia, USA (2003), pp. 53-59
Maintaining Spatial Relations in an Incremental Diagrammatic Reasoner
Ronald W. Ferguson
Joseph L. Bokor
Rudolph L. Mappus IV
Conference on Spatial Information Theory (2003), pp. 136-150
