Adaptive Weighted Deformable Part Model for Object Detection

Abstract

We describe an adaptive weighted deformable part model for object detection based on traditional deformable part model(DPM). In the original DPM model, we find that the high response score region calculated by the template filter as high-energy regions, which indicates that the influence on the detection results is greater. The parts can affect the results of object detection, some important parts may directly determine the accuracy of the results, and some unimportant parts even produce bad impacts. To reduce the adverse effects caused by unimportant part filter, we add an adaptive coefficient strategy to the traditional method, which could improve the accuracy of object detection without efficiency loss. The proposed algorithm is better in accuracy compared with the traditional deformable part model, especially in the case of occlusion, with the same efficiency.