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. (more…)