The multi-iterative closest point tracker: An online algorithm for tracking multiple interacting targets
Venue
Journal of Field Robotics, vol. 29.2 (2012), pp. 258-276
Publication Year
2012
Authors
Adam Feldman, Maria Hybinette, Tucker Balch
BibTeX
Abstract
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.
