Confidence-driven infrared target detection

Abstract The confidence of target detection can be used to evaluate the reliability and risk level of the detected targets and can effective help to exclude the false alarms, but very little investigation was involved in the past. In this letter, a confidence-driven infrared target detection method is proposed. We develop three confidence evaluating methods: (1) the median classification confidence of the cascade classifier; (2) the context confidence based on the number and the confidence of the merged detection rectangles around the detected target; and (3) the contrast confidence based on the difference between the detected target distribution and the around background distribution. The three confidences are combined to form the final confidence of the detected targets. We then use the confidence to refine the localization of the targets. The evaluation using real infrared images demonstrates the good performance of the proposed confidence-driven infrared detection algorithm on both undetected error and false alarm.

[1]  Qiling Tang,et al.  Spatiotemporal Smooth Models for Moving Object Detection , 2008, IEEE Signal Process. Lett..

[2]  Yong Chen,et al.  Real-time detection of rapid moving infrared target on variation background , 2008 .

[3]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[4]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[5]  Saralees Nadarajah,et al.  Probabilities of False Alarm and Detection for the NAMF Operating in Gaussian Clutter , 2007, IEEE Signal Processing Letters.

[6]  Pojala Chiranjeevi,et al.  New Fuzzy Texture Features for Robust Detection of Moving Objects , 2012, IEEE Signal Processing Letters.

[7]  Jing Xiao,et al.  Contextual boost for pedestrian detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Xiangzhi Bai,et al.  Analysis of new top-hat transformation and the application for infrared dim small target detection , 2010, Pattern Recognit..