Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/44927
Title: Particle PHD Filter Based Multiple Human Tracking Using Online Group-Structured Dictionary Learning
Authors: Fu, Z
Feng, P
Angelini, F
Chambers, J
Naqvi, SM
First Published: 16-Mar-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Access, 2018, 6, pp. 14764-14778
Abstract: An enhanced sequential Monte Carlo probability hypothesis density (PHD) filter-based multiple human tracking system is presented. The proposed system mainly exploits two concepts: a novel adaptive gating technique and an online group-structured dictionary learning strategy. Conventional PHD filtering methods preset the target birth intensity and the gating threshold for selecting real observations for the PHD update. This often yields inefficiency in false positives and missed detections in a cluttered environment. To address this issue, a measurement-driven mechanism based on a novel adaptive gating method is proposed to adaptively update the gating sizes. This yields an accurate approach to discriminate between survival and residual measurements by reducing the clutter inferences. In addition, online group-structured dictionary learning with a maximum voting method is used to robustly estimate the target birth intensity. It enables the new-born targets to be automatically detected from noisy sensor measurements. To improve the adaptability of our group-structured dictionary to appearance and illumination changes, we employ the simultaneous code word optimization algorithm for the dictionary update stage. Experimental results demonstrate our proposed method achieves the best performance amongst state-of-the-art random finite set-based methods, and the second best online tracker ranked on the leaderboard of latest MOT17 challenge.
DOI Link: 10.1109/ACCESS.2018.2816805
eISSN: 2169-3536
Links: https://ieeexplore.ieee.org/document/8318585
http://hdl.handle.net/2381/44927
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2018, Institute of Electrical and Electronics Engineers (IEEE). Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)
Appears in Collections:Published Articles, Dept. of Engineering

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