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

• Published in: IEEE access Vol. 6; pp. 14764 - 14778
• Main Authors: Fu, Zeyu, Feng, Pengming, Angelini, Federico, Chambers, Jonathon, Naqvi, Syed Mohsen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adaptive gating; Adaptive systems; Algorithms; birth intensity estimation; Clutter; Dictionaries; dictionary learning; Estimation; group-structured sparsity; Learning; Machine learning; Multiple human tracking; Noise measurement; Optimization; Radio frequency; Signal processing algorithms; SMC-PHD filter; Target detection; Target tracking; Tracking systems
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2816805

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.