Exploiting Simplified Depth Estimation for Stereo-based 2D Object Detection

Stereo image inputs provide higher object detection accuracy than monocular images by enabling the detection of objects that are missed from one view while being detectable from another view. To take advantage of additional information from the secondary image, it is necessary to search for the corresponding region in the images of different views by projecting with depth information of the target object. However, most existing studies utilize highly complex computations to estimate the depth for simple 2D object detection. This complexity limits the potential for deploying the methods on platforms, such as unmanned aerial vehicles, that involve significant resource constraints. In this paper, we introduce a simplified depth approximation to obtain depth information by quantizing the depth values into a small number of representative values. With these values, the regions of interest are projected to the secondary image to concatenate the information from the additional image. We validate our method with the KITTI dataset. Our results show that while having very low complexity, our approximation method leads to greatly improved object detection performance in two out of three difficulty groups of the dataset, and comparable performance in the other difficulty group compared to use of monocular image input.