关键词: 可变形部件模型, 目标检测, 稀疏表示, 稀疏编码

Abstract:

The object detection method based on the deformable part model (DPM) uses the histogram of oriented gradients (HOG) to describe features. The HOG limits the performance of the DPM, as it cannot deal with noisy edges and ignores the flat areas while focusing on edge areas. In order to improve the performance of the DPM, we propose a DPM object detection method based on sparse representation. Instead of using the HOG, the method uses sparse coding to construct a new feature descriptor. The sparse coding based feature vectors can represent more information of image patches. Experimental results show that the proposed method can improve the precision of the DPM method on the PASCAL VOC 2012 dataset.

Key words: deformable part model, object detection, sparse representation, sparse coding