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Online multi-person tracking using integral channel features

: Kieritz, Hilke; Becker, Stefan; Hübner, Wolfgang; Arens, Michael


Institute of Electrical and Electronics Engineers -IEEE-:
13th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2016 : August 23-26, 2016 in Colorado Springs, Colorado
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-3811-4
ISBN: 978-1-5090-3812-1
9 S.
International Conference on Advanced Video and Signal Based Surveillance (AVSS) <13, 2016, Colorado Springs/Colo.>
Fraunhofer IOSB ()

Online multi-person tracking benefits from using an online learned appearance model to associate detections to tracks and further to close gaps in detections. Since Integral Channel Features (ICF) are popular for fast pedestrian detection, we propose an online appearance model that is using the same features without recalculation. The proposed method uses online Multiple-Instance Learning (MIL) to incrementally train an appearance model for each person discriminating against its surrounding. We show that a low number of discriminatingly selected Integral Channel Features are sufficient to achieve state-of-the-art results on the MOT2015 and MOT2016 benchmark.