A multi-target tracking algorithm based on multiple cameras

被引：0

作者：

Jiang, Ming-Xin ^{[1
,2
]}

Wang, Hong-Yu ^{[1
]}

Liu, Xiao-Kai ^{[1
]}

机构：

[1] School of Information and Communication Engineering, Dalian University of Technology

[2] School of Information and Information and Communication, Dalian Nationalities University

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2012年 / 38卷 / 04期

关键词：

Codebook; Homography; Multi-target tracking; Multiple cameras; The shortest paths optimization;

D O I：

10.3724/SP.J.1004.2012.00531

中图分类号：

学科分类号：

摘要：

The reliable tracking of multi-targets is a challenging issue in computer vision. In this paper, we propose a novel multi-target localizing and tracking algorithm based on multiple cameras. Firstly, the view-to-view homographies are computed using several landmarks on different planes. Then, the foreground likelihood map in each view is obtained by using a codebook background modeling algorithm. Finally, we can localize multiple objects at multiple planes and perform tracking by shortest paths optimization. Compared with other popular methods, our proposed algorithm does not require computing the vanishing points of cameras. Therefore, it reduces the complexity and improves the accuracy simultaneously. Adopting the shortest path optimization algorithm can improve the tracking efficiency. The experimental results demonstrate that our method is robust to occlusion and also can achieve real-time performance. Copyright © 2012 Acta Automatica Sinica. All rights reserved.

引用

页码：531 / 539

页数：8

共 17 条

[1]

Hu W.M., Tan T.N., Wang L., Maybank S., A survey on visual surveillance of object motion and behaviors, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 34, 3, pp. 334-352, (2004)

[2]

Yilmaz A., Javed O., Shah M., Object tracking: A survey, ACM Computing Surveys, 38, 4, (2006)

[3]

Zhao T., Nevatia R., Tracking multiple humans in crowded environment, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 406-413, (2004)

[4]

Hue C., Le Cadre J.P., Perez P., Sequential Monte Carlo methods for multiple target tracking and data fusion, IEEE Transactions on Signal Processing, 50, 2, pp. 309-325, (2002)

[5]

Comaniciu D., Ramesh V., Meer P., Kernel-based object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 5, pp. 564-577, (2003)

[6]

Jiang M.X., Li M., Wang H.Y., A robust combined algorithm of object tracking based on moving object detection, Proceedings of the International Conference on Intelligent Control and Information Processing, pp. 619-622, (2010)

[7]

Mittal A., Davis L.S., M2Tracker: A multi-view approach to segmenting and tracking people in a cluttered scene, International Journal of Computer Vision, 51, 3, pp. 189-203, (2003)

[8]

Fleuret F., Berclaz J., Lengagne R., Fua P., Multicamera people tracking with a probabilistic occupancy map, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 2, pp. 267-281, (2008)

[9]

Kim K., Davis L., Multi-camera tracking and segmentation of occluded people on ground plane using search-guided particle filtering, Proceedings of the 9th European Conference on Computer Vision, pp. 98-109, (2006)

[10]

Eshel R., Moses Y., Tracking in a dense crowd using multiple cameras, International Journal of Computer Vision, 88, 1, pp. 129-143, (2010)

← 1 2 →