Human detection in far-infrared images based on histograms of maximal oriented energy map

This paper proposes a new scheme for human detection in infrared images. Firstly, we present a new segmentation method based on the image histogram cluster analysis using K-means clustering method. Then, we propose a human feature extraction method based on histograms of maximal oriented energy map using Log-Gabor wavelets as the filters for orientation selecting and use a radial basis function (RBF) based support vector machines (SVM) classifier to test the performance of our feature extraction method. The detection system is based on single frame image and doesn't need motion information of objects. The experiment results show that the algorithm is efficient.

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