Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-981-10-7299-4_23 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Human detection,Saliency algorithm,Visual attention map,Infrared thermal images,Vision application | Conference | 771 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinbo Wang | 1 | 9 | 4.06 |
| Dahai Yu | 2 | 0 | 0.68 |
| Jianfeng Han | 3 | 0 | 0.34 |
| Guoshan Zhang | 4 | 2 | 1.37 |