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Particle PHD Filter Based Multiple Human Tracking Using Online Group-Structured Dictionary Learning

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journal contribution
posted on 2019-07-19, 15:40 authored by Z Fu, P Feng, F Angelini, J Chambers, SM Naqvi
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.


This work was supported in part by the Engineering and Physical Sciences Research Council under Grant EP/K014307 and in part by MOD University Defence Research Collaboration in Signal Processing



IEEE Access, 2018, 6, pp. 14764-14778

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