Recent advances of sparse representation for object detection

Object detection is a basic and important subject in image understanding, which has attracted much attention from domestic and foreign scholars. Object detection has been widely used in military and civilian. The diversity and complexity of applications makes the traditional detection technique be affected by many factors such as complex background, noise, illumination variations, non-rigid deformation, occlusion, feeble features, scale, visual angle attitude and, etc. Recently, the developing method of sparse representation provides a novel research approach for image processing and objects detection. This paper overviews the basic concept of sparse representation and its recent progress in the theoretical study. The domestic and foreign research advances of sparse representation in object detection are summarized, especially in object feature learning, classifier and filter designing, multisource fusion detection. Meanwhile, some future directions of sparse representation in object detection are also addressed.