In 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.
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
- DOI https://doi.org/10.1007/978-981-10-7299-4_23
- Publisher Name Springer, Singapore
- Print ISBN 978-981-10-7298-7
- Online ISBN 978-981-10-7299-4
- eBook Packages Computer Science