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Open AccessArticle

Tracking Multiple Video Targets with an Improved GM-PHD Tracker

by Xiaolong Zhou 1,2, Hui Yu 3, Honghai Liu 2,* and Youfu Li 4
1
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2
School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
3
School of Creative Technologies, University of Portsmouth, Portsmouth PO1 2DJ, UK
4
Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong, China
*
Author to whom correspondence should be addressed.
Academic Editor: Lianqing Liu
Sensors 2015, 15(12), 30240-30260; https://doi.org/10.3390/s151229794
Received: 8 October 2015 / Revised: 20 November 2015 / Accepted: 24 November 2015 / Published: 3 December 2015
(This article belongs to the Special Issue Sensors for Robots)
Tracking multiple moving targets from a video plays an important role in many vision-based robotic applications. In this paper, we propose an improved Gaussian mixture probability hypothesis density (GM-PHD) tracker with weight penalization to effectively and accurately track multiple moving targets from a video. First, an entropy-based birth intensity estimation method is incorporated to eliminate the false positives caused by noisy video data. Then, a weight-penalized method with multi-feature fusion is proposed to accurately track the targets in close movement. For targets without occlusion, a weight matrix that contains all updated weights between the predicted target states and the measurements is constructed, and a simple, but effective method based on total weight and predicted target state is proposed to search the ambiguous weights in the weight matrix. The ambiguous weights are then penalized according to the fused target features that include spatial-colour appearance, histogram of oriented gradient and target area and further re-normalized to form a new weight matrix. With this new weight matrix, the tracker can correctly track the targets in close movement without occlusion. For targets with occlusion, a robust game-theoretical method is used. Finally, the experiments conducted on various video scenarios validate the effectiveness of the proposed penalization method and show the superior performance of our tracker over the state of the art. View Full-Text
Keywords: robot vision; video targets tracking; probability hypothesis density; weight penalization; multi-feature fusion robot vision; video targets tracking; probability hypothesis density; weight penalization; multi-feature fusion
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Zhou, X.; Yu, H.; Liu, H.; Li, Y. Tracking Multiple Video Targets with an Improved GM-PHD Tracker. Sensors 2015, 15, 30240-30260.

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