Occlusion Handling Human Detection with Refocused Images

摘要

The paper presents a novel robust human detection method based on camera array system to broaden the application range for human detection. Currently, even by using a deep neural network (DNN), it is difficult to detect a hardly occluded human. In the camera array system, we consider how to distinctly show a human occluded by an environmental condition. The generated refocused images by the camera array system allow us to remove the effect of the noises. Although refocused images have not been utilized in conventional human detection, we believe that the refocused images are beneficial for improving the detection performance, especially in severe conditions. To execute the experiments, we have collected Refocused Human DataBase (RHDB) with the camera array system. By using HOG+SVM with a monocular camera (at an almost random rate of 54.8%), the refocused images made the +10.1% improvement (64.9%) by noticeably showing a human. The combined representation of refocused images and AlexNet achieved 94.6% on the RHDB. Moreover, our final model recorded 98.0% with an attention-layer and fine-tuned parameters.