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Inferring tracklets for multi-object tracking

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3 Author(s)
Prokaj, J. ; Univ. of Southern California, Los Angeles, CA, USA ; Duchaineau, M. ; Medioni, G.

Recent work on multi-object tracking has shown the promise of tracklet-based methods. In this work we present a method which infers tracklets then groups them into tracks. It overcomes some of the disadvantages of existing methods, such as the use of heuristics or non-realistic constraints. The main idea is to formulate the data association problem as inference in a set of Bayesian networks. This avoids exhaustive evaluation of data association hypotheses, provides a confidence estimate of the solution, and handles split-merge observations. Consistency of motion and appearance is the driving force behind finding the MAP data association estimate. The computed tracklets are then used in a complete multi-object tracking algorithm, which is evaluated on a vehicle tracking task in an aerial surveillance context. Very good performance is achieved on challenging video sequences. Track fragmentation is nearly non-existent, and false alarm rates are low.

Published in:

Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on

Date of Conference:

20-25 June 2011