title: Tracking Multiple Video Targets with an Improved GM-PHD Tracker.
abstract: 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.
authors.affiliation: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China. zxl@zjut.edu.cn.|School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK. zxl@zjut.edu.cn.
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Zhou
authors.affiliation: School of Creative Technologies, University of Portsmouth, Portsmouth PO1 2DJ, UK. hui.yu@port.ac.uk.
authors.forename: Hui
authors.initials: H
authors.lastname: Yu
authors.affiliation: School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK. honghai.liu@port.ac.uk.
authors.forename: Honghai
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong, China. meyfli@cityu.edu.hk.
authors.forename: Youfu
authors.initials: Y
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D058948:Cell Tracking; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D009068:Movement; D016011:Normal Distribution; D012371:Robotics / Q000379:methods*; D014741:Video Recording / Q000379:methods*
keywords: multi-feature fusion; probability hypothesis density; robot vision; video targets tracking; weight penalization