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Lookup NU author(s): Zeyu Fu, Pengming Feng, Federico Angelini, Professor Jonathon Chambers, Dr Mohsen Naqvi
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
An enhanced Sequential Monte Carlo probability hypothesis density (SMC-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. Conven-tional 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 automati-cally detected from noisy sensor measurements. To improve the adaptability of our group-structured dictionary to appearance and illumination changes, we employ the Simultaneous Codeword Optimization (SimCO) algorithm for the dictionary update stage. Experimental results demonstrate our proposed method achieves the best performance amongst state-of-the-art Random Finite Set (RFS) based methods, and the second best online tracker ranked on the leaderboard of latest MOT17 Challenge.
Author(s): Fu Z, Feng P, Angelini F, Chambers JC, Naqvi SM
Publication type: Article
Publication status: Published
Journal: IEEE Access
Year: 2018
Volume: 6
Pages: 14764-14778
Online publication date: 16/03/2018
Acceptance date: 03/03/2018
Date deposited: 14/03/2018
ISSN (electronic): 2169-3536
Publisher: IEEE
URL: https://doi.org/10.1109/ACCESS.2018.2816805
DOI: 10.1109/ACCESS.2018.2816805
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