Inferring tracklets for multi-object tracking

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...

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Bibliographic Details
Published inCVPR 2011 WORKSHOPS pp. 37 - 44
Main Authors Prokaj, J., Duchaineau, M., Medioni, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Approximation algorithms
Inference algorithms
Joints
Noise
Position measurement
Tracking
Velocity measurement
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Summary: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.
ISBN:9781457705298
145770529X
ISSN:2160-7508
2160-7516
DOI:10.1109/CVPRW.2011.5981753