An Effective Imaging System for 3D Detection of Occluded Objects.

Occluded objects detection is a challenge task in computer vision. To address this problem, this paper proposes an effective light field imaging system for occluded objects 3D detection, which integrates digital refocus methods to imaging occluded objects and deep learning based method to located objects position with defocus clues. Camera arrays based integral imaging system could provide focal stacks images, which makes occluded objects more clear and attenuates foreground occlusion. With observation that recognition probability are related to objects clarity, as well as focal length of images, recognition probability based defocus clues are proposed to located objects depth. Hierarchical object localization process is applied on refocus images stacks to coarsely located object depth by detected probabilities, following gradient based fine-grained defocus response process could further refine the depth accuracy. With the depths from defocus clues and detected locations from neural model, proposed algorithm could achieve 3D object detection under partial occlusion. Furthermore, a parallel computation framework is proposed to accelerate whole detection process. Real experiments show the robust performance of proposed 3D occluded objects detection algorithm.