Laser-based tracking of multiple interacting pedestrians via on-line learning

Successful multi-target tracking requires locating the targets and labeling their identities. For a laser-based tracking system, this mission becomes significantly challenging when the targets are in close proximity or frequently interact with one another. This paper presents a novel on-line learning-based method for laser-based multi-target tracking. When the targets do not interact with one another, multiple independent trackers are employed to train a classifier for each target. When the targets are in close proximity, the learned classifiers are used to assist in tracking. The tracking and learning supplement each other in the proposed method, which is helpful in dealing with difficult problems encountered in laser-based multi-target tracking; moreover, it ensures that the entire process can be completely automatic and available on-line. Various evaluations have demonstrated that this method performs better than previous methods when interactions occur, and it can maintain the correct tracking under various complex tracking situations.