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.