A Fast Deep-space Infrared Multi-target Detection Algorithm Based on Clustering

Ye You-Shi; Tang  Lin-Bo; Zhao  Bao-Jun

doi:10.3724/SP.J.1146.2010.00175

Volume 33 Issue 1

Feb. 2011

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Ye You-Shi, Tang Lin-Bo, Zhao Bao-Jun. A Fast Deep-space Infrared Multi-target Detection Algorithm Based on Clustering[J]. Journal of Electronics & Information Technology, 2011, 33(1): 77-84. doi: 10.3724/SP.J.1146.2010.00175

Citation:

Ye You-Shi, Tang Lin-Bo, Zhao Bao-Jun. A Fast Deep-space Infrared Multi-target Detection Algorithm Based on Clustering[J]. Journal of Electronics & Information Technology, 2011, 33(1): 77-84. doi: 10.3724/SP.J.1146.2010.00175

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A Fast Deep-space Infrared Multi-target Detection Algorithm Based on Clustering

doi: 10.3724/SP.J.1146.2010.00175

Received Date: 2010-03-01
Rev Recd Date: 2010-06-28
Publish Date: 2011-01-19

Abstract

Abstract

This paper presents a fast real-time multi-target detection algorithm based on line target clustering. Adaptive threshold is applied to image segmentation; And then enclosing rectangle prosthetics, line target extraction and clustering merger are utilized for the binary image to implement full-field pixel-level targets detection and conduct ID tag. So undetected problems caused by the traditional detection algorithm can be avoid; Finally, a five points square predictor and cost function are constructed for trajectory matching, by which the problems of multi-target division, cross, temporarily lost due to overlap and so on are effectively resolved. The experiments are carried on SOPC hardware platform and the results show that the proposed algorithm can perform real-time detection accurately for the deep-space objects.
- Deep-space multi-target detection,
- Enclosing rectangle prosthetics,
- Clustering merger,
- SOPC (System On Programmable Chip)

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References(1)

References

李毅, 孙正兴, 远博. 一种改进的帧差和背景减相结合的运动检测方法[J]. 中国图象图形学报, 2009, 14(6): 1162-1168.Li Yi, Sun Zheng-xing, and Yuan Bo. An improved method for motion detection by frame difference and background subtraction [J]. Journal of Image and Graphics, 2009, 14(6): 1162-1168.[2]王兆魁, 张育林. 一种CCD星图星点快速定位算法[J]. 空间科学学报, 2006, 26(3): 209-214.Wang Zhao-kui and Zhang Yu-lin. Algorithm for CCD star image rapid locating[J]. Chinese Journal of Space Science, 2006, 26(3): 209-214.[3]Fejes S and Davis L S. What can projections of flow fields tell us about the visual motion [C]. Proceeding of International Conference on Computer Vision, Bombay, India, 1998: 979-986.[4]王勇，赵保军. 复杂背景下低信噪比点目标的实时检测算法及实现[J]. 系统工程与电子技术, 2002, 24(12): 18-21.Wang Yong and Zhao Bao-jun. A real-time detection algorithm for low SNR point targets in a complex background [J]. Systems Engineering and Electronics, 2002, 24(12): 18-21.[5]孙吉贵, 刘杰, 赵连宇. 聚类算法研究[J]. 软件学报, 2008, 19(1): 48-61.Sun Ji-gui, Liu Jie, and Zhao Lian-yu. Clustering algorithms research [J].Journal of Software.2008, 19(1):48-61[6]王春歆, 沈同圣, 张玉叶. 基于层次聚类的弱小目标检测算法[J]. 计算机工程与应用, 2008, 44(19): 24-27.Wang Chun-xin, Shen Tong-sheng, and Zhang Yu-ye. Small targets detection based on hierarchical clustering [J]. Computer Engineering and Applications, 2008, 44(19): 24-27.[7]Murat T. A. Digital Video Processing [M]. New York: Prentice-Hall Press, 1996: 206-218.[8]蔡征, 黄瑞光. 运动图像序列的多目标跟踪技术及实现[J]. 计算机与数字工程, 2006, 34(9): 140-143.Cai Zheng and Huang Rui-guang. The research and implementation on multi-target tracking in image sequences[J].Computer Digital Engineering.2006, 34(9):140-143[9]刘玉琏. 数学分析: 下册 [M]. 北京: 高等教育出版社, 2008: 189-208.

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