Detecting Small Scale Pedestrians and Anthropomorphic Negative Samples Based on Light-Field Imaging

Great progress has been made in the field of pedestrian detection, but the following two problems have not yet been well addressed. One problem is the missed detection of small scale pedestrain as false negative failure, and the other one is confusion with anthropomorphic negative samples like vertical structures as false positive failure. In this paper, to tackle the above two problems, we use the light-field camera to capture pedestrian images for the following reasons: (i) the light-field camera can obtain multi-depth refocused images in a single exposure using one sensor, (ii) compared with 2D images, these refocused images can provide different key representations for different parts of the image. We further establish a light-field pedestrian dataset with 1766 images for pedestrian detection. A multi-focus detection network proposed in this work consists of multiple-branch detection models and takes multiple refocused images as inputs. In order to select the appropriate candidate proposal bounding box as final detection results, we design a cumulative probability selection (CPS) layer to combine each refocused image branch and accumulate the probability of each candidate neighboring proposal. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods on our light-field pedestrian dataset.