Human tracking method based on maximally stable extremal regions with multi-cameras

被引：0
|
作者：
Zhang L. [1 ,2 ]
Liu J.-L. [1 ]
机构：
[1] School of Information Science and Electronic Engineering, Zhejiang University
[2] School of Computer Science, Hangzhou Dianzi University
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2010年 / 44卷 / 06期
关键词：
Human tracking; Maximally stable extremal region(MSER); Region matching; Video surveillance;
D O I：
10.3785/j.issn.1008-973X.2010.06.007
中图分类号：
学科分类号：
摘要：
A human tracking method based on maximally stable extremal region (MSER)was established in order to realize the human tracking with multi-cameras in the video surveillance system. The proposed method uses gray information to reduce the difference of the cameras' gains and their light spectrum property. The approach transforms the human tracking into elliptic region matching. The method does elliptic region fitting to each maximally stable extremal region(MSER), and then selects the elliptic regions which meet some constraints. These selected elliptic regions are normalized to unity circular regions by whitening of covariance matrix. The right matched elliptic regions are obtained by rotational invariant vectors calculation, histogram density estimation and weighted average distance calculation. Thus, the human tracking across cameras is realized. Experimental results show that the approach can effectively realize the human tracking with multi-cameras.
引用
收藏
页码：1091 / 1097
页数：6
相关论文
共 9 条
  • [1] Javed O., Rasheed Z., Shafique K., Et al., Tracking across multiple cameras with disjoint views, Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 952-957, (2003)
  • [2] Khan S., Shan M., Consistent labeling of tracked objects in multiple cameras with overlapping fields of view, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 10, pp. 1355-1360, (2003)
  • [3] Fraundorfer F., Bischof H., Evaluation of local detectors on non-planar scenes, Proceedings of the 28th Workshop of the Austrian Association for Pattern Recognition, pp. 125-132, (2004)
  • [4] Mikolajczyk K., Tuytelaars T., Schmid C., Et al., A comparison of affine region detectors, International Journal of Computer Vision, 65, 1-2, pp. 43-72, (2005)
  • [5] Moreels P., Perona P., Evaluation of features detectors and descriptors based on 3D objects, Proceedings of International Conference on Computer Vision, pp. 800-807, (2005)
  • [6] Matas J., Chum O., Urban M., Et al., Robust wide baseline stereo from maximally stable extremal regions, Image and Vision Computing, 22, 10, pp. 761-767, (2004)
  • [7] Matas J., Bilek P., Chum O., Rotational invariants for wide-baseline stereo, (2007)
  • [8] Panero J., Zelnik M., Human Dimension and Interior Space (Hardcover), (1979)
  • [9] Lowe D.G., Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, 60, 2, pp. 91-110, (2004)
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