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Optical flow detection based on enhanced fuzzy clustering with elastic grouping logic
Lu Yu; Zhang Jina; Zhang Yong; Wu Qinzhang
Volume7383
2009
Language英语
Indexed ByEi
Subtype会议论文
AbstractIn an optical flow field, the background and moving objects present different vector groups with different directions, velocities and region areas. The idea optical flow field is not easy to obtain for some kinds of reasons; in practical field, the motion vectors present confusion and uncertainty to some extent. The fuzzy clustering provides an effective way to process unclear classification. It maps every vector into every group, and the ascription presents a degree a vector belongs to a group. However, conventional fuzzy clustering method needs to determine the group number, namely the moving objects number in the view field. Before all samples are processed and the group number is fixed during iteration. The unsuitable number easily results in inaccurate segmentation. In view of this problem, an enhanced detection algorithm using fuzzy clustering with elastic grouping logic is proposed. To be called elastic grouping logic, it means that in the process of optical flow field detection, according to the ascription the vector to each group, together with the vector's location, direction and magnitude, the group number, namely the moving object number, is selfadaptively generated, and further to achieve the moving objects segmentation with precision. A stability model of motion vectors for an object group and the group's partition is also established. The experimental results illustrate the proposed algorithm is able to satisfy the need of multi-objects detection and locate the moving objects successfully.; In an optical flow field, the background and moving objects present different vector groups with different directions, velocities and region areas. The idea optical flow field is not easy to obtain for some kinds of reasons; in practical field, the motion vectors present confusion and uncertainty to some extent. The fuzzy clustering provides an effective way to process unclear classification. It maps every vector into every group, and the ascription presents a degree a vector belongs to a group. However, conventional fuzzy clustering method needs to determine the group number, namely the moving objects number in the view field. Before all samples are processed and the group number is fixed during iteration. The unsuitable number easily results in inaccurate segmentation. In view of this problem, an enhanced detection algorithm using fuzzy clustering with elastic grouping logic is proposed. To be called elastic grouping logic, it means that in the process of optical flow field detection, according to the ascription the vector to each group, together with the vector's location, direction and magnitude, the group number, namely the moving object number, is selfadaptively generated, and further to achieve the moving objects segmentation with precision. A stability model of motion vectors for an object group and the group's partition is also established. The experimental results illustrate the proposed algorithm is able to satisfy the need of multi-objects detection and locate the moving objects successfully.
Conference NameProceedings of SPIE
Conference Date2009
Document Type会议论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/7506
Collection光电测控技术研究室(三室)
Corresponding AuthorLu Yu
Affiliation中国科学院光电技术研究所
Recommended Citation
GB/T 7714
Lu Yu,Zhang Jina,Zhang Yong,et al. Optical flow detection based on enhanced fuzzy clustering with elastic grouping logic[C],2009.
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