Robust Online Multiobject Tracking With Data Association and Track Management

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In this paper, we consider a multiobject tracking problem in complex scenes. Unlike batch tracking systems using detections of the entire sequence, we propose a novel online multiobject tracking system in order to build tracks sequentially using online provided detections. To track objects robustly even under frequent occlusions, the proposed system consists of three main parts: 1) visual tracking with a novel data association with a track existence probability by associating online detections with the corresponding tracks under partial occlusions; 2) track management to associate terminated tracks for linking tracks fragmented by long-term occlusions; and 3) online model learning to generate discriminative appearance models for successful associations in other two parts. Experimental results using challenging public data sets show the obvious performance improvement of the proposed system, compared with other state-of-the-art tracking systems. Furthermore, extensive performance analysis of the three main parts demonstrates effects and usefulness of the each component for multiobject tracking.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2014-07
Language
English
Article Type
Article
Keywords

MULTIPLE OBJECT TRACKING; MULTITARGET TRACKING; VISUAL TRACKING; DETECTION RESPONSES; APPEARANCE MODELS; PARTICLE FILTER; TARGETS; PATTERNS

Citation

IEEE TRANSACTIONS ON IMAGE PROCESSING, v.23, no.7, pp.2820 - 2833

ISSN
1057-7149
DOI
10.1109/TIP.2014.2320821
URI
http://hdl.handle.net/10203/240789
Appears in Collection
ME-Journal Papers(저널논문)
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