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Sensors 2017, 17(3), 617; doi:10.3390/s17030617

Conditional Random Field (CRF)-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
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Received: 6 December 2016 / Revised: 13 March 2017 / Accepted: 14 March 2017 / Published: 17 March 2017
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
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Abstract

Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT). In particular, online MOT is more demanding due to its diverse applications in time-critical situations. A main issue of realizing online MOT is how to associate noisy object detection results on a new frame with previously being tracked objects. In this work, we propose a multi-object tracker method called CRF-boosting which utilizes a hybrid data association method based on online hybrid boosting facilitated by a conditional random field (CRF) for establishing online MOT. For data association, learned CRF is used to generate reliable low-level tracklets and then these are used as the input of the hybrid boosting. To do so, while existing data association methods based on boosting algorithms have the necessity of training data having ground truth information to improve robustness, CRF-boosting ensures sufficient robustness without such information due to the synergetic cascaded learning procedure. Further, a hierarchical feature association framework is adopted to further improve MOT accuracy. From experimental results on public datasets, we could conclude that the benefit of proposed hybrid approach compared to the other competitive MOT systems is noticeable. View Full-Text
Keywords: visual sensors; multiple object tracking; data association; conditional random fields; boosting algorithms; hybrid approaches visual sensors; multiple object tracking; data association; conditional random fields; boosting algorithms; hybrid approaches
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MDPI and ACS Style

Yang, E.; Gwak, J.; Jeon, M. Conditional Random Field (CRF)-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning. Sensors 2017, 17, 617.

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