Infrared dim moving target tracking via improved context learning

Jin Qian; Huixin Zhou; Kun Qian; Dong Zhao; Hanlin Qin; Shangzhen Song; Zhenwu Wu

doi:10.1117/12.2268553

8 March 2017 Infrared dim moving target tracking via improved context learning

Jin Qian, Huixin Zhou, Kun Qian, Dong Zhao, Hanlin Qin, Shangzhen Song, Zhenwu Wu

Proceedings Volume 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016; 1025550 (2017) https://doi.org/10.1117/12.2268553
Event: Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 2016, Jinhua, Suzhou, Chengdu, Xi'an, Wuxi, China

Abstract

It is very critical that make full use of the local information for infrared dim and small target tracking. In this paper, an effective and fast algorithm based on the context learning is proposed to track infrared dim moving target. Firstly, the principle of the spatio-temporal context learning algorithm is described and the tracking deviation is analyzed. Then, a correlation filter is utilized to get a rational context prior for the dim moving target, the advantage is that the prior considers the image intensity information between a target and its surround pixels. Furthermore, a Gaussian high-pass filter is introduced to extract an accurate spatial context, which has little influence caused by the cluttered background. At last, the tracking problem is posed by computing a confidence map which takes into account sufficient information of a dim target and its surround background. Since the proposed algorithm is realized using fast Fourier transform, it is easy to be real-time. The experiments on various clutter background sequences have validated the tracking capability of the proposed method. The experimental results show that the proposed method can provide a higher accuracy and speed than several classical algorithms, including the improved Template Matching algorithm, the Temporal-Spatial Fusion Filtering algorithm and the Moving Pipeline Filtering algorithm.

Citation Download Citation

Jin Qian, Huixin Zhou, Kun Qian, Dong Zhao, Hanlin Qin, Shangzhen Song, and Zhenwu Wu "Infrared dim moving target tracking via improved context learning", Proc. SPIE 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 1025550 (8 March 2017); https://doi.org/10.1117/12.2268553

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KEYWORDS

Detection and tracking algorithms

Clouds

Infrared radiation

Target detection

Infrared imaging

Infrared search and track

Linear filtering

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