Optical flow detection based on enhanced fuzzy clustering with elastic grouping logic

Yu Lu; Jin Zhang; Yong Zhang; Qinzhang Wu

doi:10.1117/12.828727

20 May 2009 Optical flow detection based on enhanced fuzzy clustering with elastic grouping logic

Yu Lu, Jin Zhang, Yong Zhang, Qinzhang Wu

Proceedings Volume 7283, 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment; 72832U (2009) https://doi.org/10.1117/12.828727
Event: AOMATT 2008 - 4th International Symposium on Advanced Optical Manufacturing, 2008, Chengdu, Chengdu, China

Abstract

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.

Citation Download Citation

Yu Lu, Jin Zhang, Yong Zhang, and Qinzhang Wu "Optical flow detection based on enhanced fuzzy clustering with elastic grouping logic", Proc. SPIE 7283, 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, 72832U (20 May 2009); https://doi.org/10.1117/12.828727

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KEYWORDS

Optical flow

Fuzzy logic

Logic

Detection and tracking algorithms

Motion detection

Interference (communication)

Motion models

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