Feature Points Based Multiple Moving Targets Tracking

Gao Tao; Liu Zheng-guang; Zhang Jun; Yue Shi-hong

doi:10.3724/SP.J.1146.2008.01755

Volume 32 Issue 5

May 2010

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Gao Tao, Liu Zheng-guang, Zhang Jun, Yue Shi-hong. Feature Points Based Multiple Moving Targets Tracking[J]. Journal of Electronics & Information Technology, 2010, 32(5): 1111-1115. doi: 10.3724/SP.J.1146.2008.01755

Citation:

Gao Tao, Liu Zheng-guang, Zhang Jun, Yue Shi-hong. Feature Points Based Multiple Moving Targets Tracking[J]. Journal of Electronics & Information Technology, 2010, 32(5): 1111-1115. doi: 10.3724/SP.J.1146.2008.01755

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Feature Points Based Multiple Moving Targets Tracking

doi: 10.3724/SP.J.1146.2008.01755

Received Date: 2008-12-22
Rev Recd Date: 2010-03-04
Publish Date: 2010-05-19

Abstract

Abstract

For the widely demanding of adaptive multiple moving targets tracking, a type of feature based multi-target tracking algorithm is presented. Background is adaptively modeled by Marr wavelet kernel function and a background subtraction technique based on redundant discrete wavelet transforms is used to detect multiple moving targets. A type of particle filtering combined with SIFT key points is used for tracking, and a queue chain method is used to record data association among different targets, which can improve the detection accuracy and reduce the complexity. Actual tests show that the algorithm can track multi-target with a better performance of real time and mutual occlusion robustness; it can be used in intelligent monitoring with extensive application prospect.

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References(1)

References

Betke M, Haritaoglu E, and Davis L S. Real-time multiple vehicle detection and tracking from a moving vehicle [J].Machine Vision and Applications.2000, 12(2):69-83[2]Lin Ming-xiu and Xu Xin-he. Multiple vehicle visual tracking from a moving vehicle [C][J].Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, Jinan, China.2006, 2:373-378[3]孙志海, 朱善安. 多视频运动对象实时分割及跟踪技术[J]. 浙江大学学报(工学版), 2008, 42(9): 1631-1635.Sun Zhi-hai and Zhu Shan-an. Real-time segmentation and tracking technique of multiple moving objects in video sequence [J]. Journal of Zhejiang University (Engineering Science), 2008, 42(9): 1631-1635.[4]Lin Shin-ping, Chen Yuan-hsin, and Wu Bing-fei. Real-time multiple vehicle detection and tracking system with prior occlusion detection and resolution, and prior queue detection and resolution [C]. Proceedings of 18th International Conference on Pattern Recognition(ICPR), Hong Kong, 2006, 1: 828-831.[5]Jin Yong-gang and Mokhtarian F. Variational particle filter for multi-object tracking [C]. Proceedings of IEEE 11th International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil, 2007: 1-8.[6]Pinkiewicz T, Williams R, and Purser J. Application of the particle filter to tracking of fish in aquaculture research [C]. Proceedings of Digital Image Computing: Techniques and Applications (DICTA), Canberra, 2008: 457-464.Gao Tao and Liu Zheng-guang. Moving video object segmentation based on redundant wavelet transform [C]. Proceedings of the IEEE International Conference on Information and Automation, Zhangjiajie, China, 2008: 156-160.[7]Lowe D G. Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision.2004, 60(2):91-110[8]Nummiaro K. An adaptive color-based particle filter[J].Image and Vision Computing.2003, 21(1):99-110[9]Reckleitis I. A particle filter tutorial for mobile robot localization [C]. Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan, 2003, 42: 1-36.

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