Detecting and tracking dim small targets in infrared image sequences under complex backgrounds
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This paper presents a unified framework for automatically detecting and tracking dim small targets in infrared (IR) image sequence under complex backgrounds. Firstly, the variance weighted information entropy (variance WIE) followed by a region growing technique is introduced to segment the candidate targets in a single-frame IR image after background suppression. Then the pipeline filter is used to verify the real targets. The position and the size of the detected target are then obtained to initialize the tracking algorithm. Secondly, we adopt an improved local binary pattern (LBP) scheme to represent the target texture feature and propose a joint gray-texture histogram method for a more distinctive and effective target representation. Finally, target tracking is accomplished by using the mean shift algorithm. Experimental results indicate that the proposed method can effectively detect the dim small targets under complex backgrounds and has better tracking performance compared with the gray histogram based tracking methods such as the mean shift and the particle filtering.
KeywordsDim small target detection and tracking Variance weighted information entropy (variance WIE) Local binary pattern (LBP) Mean shift
We are very grateful to the anonymous reviewers for their constructive comments and suggestions that help improve the quality of this manuscript. We also appreciate the providers of the test sequences. This works was supported by the National Natural Science Foundation of China (No. 60873086), and the Aeronautics Science Foundation of China (No. 2011ZD53049).
- 11.Laura SL, Erik LM (2012) Distribution fields for tracking. In: Proc. of the IEEE Comput Vis Pattern Recogn, pp 1910–1917Google Scholar
- 19.OTCBVS benchmark dataset. [online] http://www.cse.ohio-state.edu/otcbvs-bench
- 22.Shao XP, Fan H, Lu GX, Xu J (2012) An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system. Infrared Phys Technol, Available online 15 June 2012Google Scholar
- 24.Valtteri V, Pietikainen M (2007) Multi-object tracking using color, texture and motion. In: Proc. of the IEEE Comput Vis Pattern Recogn, pp 1–7Google Scholar
- 25.VIVID database. [online] http://vision.cse.psu.edu/data/vividEval/datasets/datasets.html
- 26.Wang SP (2010) Adaptive feature selection for infrared object tracking. Wireless Communications Networking and Mobile Computing, pp 1–4Google Scholar
- 28.Wang JQ, Yagi Y (2006) Integrating shape and color features for adaptive real-time target tracking. In: Proc. of the IEEE Robotics and Biomimetics, pp 1–6Google Scholar