Learning discriminated and correlated patches for multi-view object detection using sparse coding

Multi-view object detection is an open and challenging problem due to its inherent intra-class variability among discrete viewpoints. This paper aims to perform multi-view object detection by learning discriminated and correlated patches firstly and then making inference based on them. In the training stage, discriminative patches are discovered for each view by a Hough decision tree corresponding to leaf nodes with high distinctiveness and stable spatial distributions in the tree. Then discriminated patches across different views are linked to establish the correlations between any two neighboring views. During multi-view detection, intra-view direct votes and inter-view transfer votes are integrated to obtain voted Hough images through a probabilistic approach with each view having one Hough image, and Mean-Shift estimation is finally employed to detect object instances and infer image viewpoint. The experiments performed on two benchmark multi-view 3D Object Category datasets and PASCAL VOC'06 Car dataset illustrate the effectiveness of the proposed framework.