Research of real time dim target detection algorithm under occlusion in space background

Lei Li; Yan-ning Zhang; Jin-qiu Sun; Rui Yao

doi:10.1117/12.897575

15 August 2011 Research of real time dim target detection algorithm under occlusion in space background

Lei Li, Yan-ning Zhang, Jin-qiu Sun, Rui Yao

Proceedings Volume 8196, International Symposium on Photoelectronic Detection and Imaging 2011: Space Exploration Technologies and Applications; 819608 (2011) https://doi.org/10.1117/12.897575
Event: International Symposium on Photoelectronic Detection and Imaging 2011, 2011, Beijing, China

Abstract

In this paper, a real time method for detecting multiple dim targets in deep space background is presented and special attention is paid to occlusion handling. We matched the stars in tow continuous images to get their speed at first and found moving target pairs through speed in both images, a kalman filter whose equation was updated by the centroid was adopted to track the target. The star's area was used to judge occlusion, a two Gaussian mixture model was build using the pixels' gray value of fusing region and we used the predicted value which the kalman filter given to detect the target. The model's parameters were estimated using the expectation-maximization method and applied to separate the target and the star as well as computing the precise centroid. Extensive experiments on real images sequences show that the proposed approach could effectively meet the requirements of the real-time detection with a low false alarm rate and a high detection probability, simulation results show that it can also create a accuracy centroid when occlusion happens.

Citation Download Citation

Lei Li, Yan-ning Zhang, Jin-qiu Sun, and Rui Yao "Research of real time dim target detection algorithm under occlusion in space background", Proc. SPIE 8196, International Symposium on Photoelectronic Detection and Imaging 2011: Space Exploration Technologies and Applications, 819608 (15 August 2011); https://doi.org/10.1117/12.897575

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