Human articulated body recognition method in high-resolution monitoring images

Building recognition is a crucial and still challenging problem in computer vision and pattern recognition field, especially in human articulate body recognition. In this paper, we present a novel formulation for detecting articulated human bodies from high-resolution monitoring images. Firstly, we use a coarse detection process to detect the human object patches from monitoring images. To build a descriptor which contain color frequency information based on histograms of oriented gradient method. Then, a linear support vector machine was used to speed up detecting large image patches which contain the object joints. Secondly, towards an efficient detection, a refinement classification method is proposed to determine the patches that actually contain objects. Finally, to increase the size of image patches in order that the whole human objects can be included, an accelerated and improved salient mask is used to improve the performance of the dense scale-invariant feature transform descriptor. Experimental results on three datasets demonstrated our method effectiveness.