Optical flow detection based on enhanced fuzzy clustering with elastic grouping logic
文献类型：会议论文
| 作者 | Lu Yu; Zhang Jina; Zhang Yong; Wu Qinzhang |
| 出版日期 | 2009 |
| 会议名称 | Proceedings of SPIE |
| 会议日期 | 2009 |
| 卷号 | 7383 |
| 通讯作者 | Lu Yu |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.ioe.ac.cn/handle/181551/7506]  |
| 专题 | 光电技术研究所_光电探测技术研究室（三室） |
| 作者单位 | 中国科学院光电技术研究所 |
| 推荐引用方式 GB/T 7714 | Lu Yu,Zhang Jina,Zhang Yong,et al. Optical flow detection based on enhanced fuzzy clustering with elastic grouping logic[C]. 见:Proceedings of SPIE. 2009. |
入库方式： OAI收割
来源：光电技术研究所
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