NDLI: An improved GM-PHD tracker with track management for multiple target tracking

Content Provider	IEEE Xplore Digital Library
Author	Huanqing Zhang Jinlong Yang Hongwei Ge Le Yang
Copyright Year	2015
Description	Author affiliation: Sch. of Internet of Things Eng., Jiangnan Univ., Wuxi, China (Huanqing Zhang; Jinlong Yang; Hongwei Ge; Le Yang)
Abstract	The probability hypothesis density (PHD) filter is a promising tool for tracking the time-varying number of targets in real time. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. By using Gaussian component labels in GM-PHD filter, the identities of individual target can be obtained. However, the labeling GM-PHD filter cannot correctly discriminate tracks of individual targets when targets are moving closely to each other. To solve this problem, an improved GM-PHD tracker is proposed, which is able to identify and manage the track of individual target effectively. First, a detection-guided dynamic reweight scheme is employed in the GM-PHD filter to alleviate the weight error of closely spaced targets. Then, a novel track management scheme is introduced to form and maintain the tracks of individual targets. The simulation results demonstrate the better performance of the proposed algorithm in comparison with the labeling GM-PHD filter.
Starting Page	185
Ending Page	190
File Size	345315
Page Count	6
File Format	PDF
e-ISBN	9781479998920
DOI	10.1109/ICCAIS.2015.7338659
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Heuristic algorithms Multi-target tracking Random finite set Filtering algorithms Information filters Filtering theory Track management Gaussian mixture PHD
Content Type	Text
Resource Type	Article

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