A novel multi-object detection method in complex scene using synthetic aperture imaging

This paper proposes a novel multi-object detection method using multiple cameras. Unlike conventional multi-camera object detection methods, our method detects multiple objects using a linear camera array. The array can stream different views of the environment and can be easily reconfigured for a scene compared with the overhead surround configuration. Using the proposed method, the synthesized results can provide not only views of significantly occluded objects but also the ability of focusing on the target while blurring objects that are not of interest. Our method does not need to reconstruct the 3D structure of the scene, can accommodate dynamic background, is able to detect objects at any depth using a new synthetic aperture imaging method based on a simple shift transformation, and can see through occluders. The experimental results show that the proposed method has a good performance and can synthesize objects located within any designated depth interval with much better clarity than that using an existing method. To our best knowledge, it is the first time that such a method using synthetic aperture imaging has been proposed and developed for multi-object detection in a complex scene with a significant occlusion at different depths.