Efficient depth localization of objects in a 3D space using computational integral imaging

Accurate localization and recognition of objects in the three dimensional (3D) space can be useful in security and defence applications such as scene monitoring and surveillance. A main challenge in 3D object localization is to find the depth location of objects. We demonstrate here the use of a camera array with computational integral imaging to estimate depth locations of objects detected and classified in a two-dimensional (2D) image. Following an initial 2D object detection in the scene using a pre-trained deep learning model, a computational integral imaging is employed within the detected objects' bounding boxes, and by a straightforward blur measure analysis, we estimate the objects' depth locations.