A Saliency Based Human Detection Framework for Infrared Thermal Images

Abstract

A Saliency Based Human Detection Framework for Infrared Thermal Images. In: Yang J. et al. (eds) Computer Vision. CCCV 2017. Communications in Computer and Information Science, vol 771. Springer, SingaporeIn this paper, a novel saliency framework for crowd detection in infrared thermal images is proposed. In order to obtain the optimal classifier from a large amount of data, the process of training consists of the following four steps:

(a) a saliency contrast algorithm is employed to detect the regions of interest;
(b) standard HOG features of the selected interest areas are extracted to represent the human object; (c) the extracted features, which are prepared for training, are optimized based on a visual attention map;
(d) a support vector machine (SVM) algorithm is applied to compute the classifier.

Finally, we can detect the human precisely after high-saliency areas of an image are input into the classifier.

In order to evaluate our algorithm, we constructed an infrared thermal image database collected by a real-time inspection system.

The experimental results demonstrated that our method can outperform the previous state-of-the art methods for human detection in infrared thermal images, and the visual attentional techniques can effectively represent prior knowledge for features optimization in a practicable system.

Authors: Xinbo Wang (123), Dahai Yu (123)Email author Jianfeng Han(123), Guoshan Zhang (123)

Affiliations: 1.Tianjin University Tianjin, China, 2.Tianjin ShenQi Technology Co., Ltd.Tianjin China, 3.Tianjin University of Commerce Tianjin, China.

Conference paper

Part of the Communications in Computer and Information Science book series (CCIS, volume 771)

Citation: Wang X., Yu D., Han J., Zhang G. (2017) A Saliency Based Human Detection Framework for Infrared Thermal Images. In: Yang J. et al. (eds) Computer Vision. CCCV 2017. Communications in Computer and Information Science, vol 771. Springer, Singapore

 

Source: https://link.springer.com/chapter/10.1007/978-981-10-7299-4_23