Recognizing People In Blind Spots Based On Surrounding Behavior

Recent advances in computer vision have achieved remarkable performance improvements. These technologies mainly focus on recognition of visible targets. However, there are many invisible targets in blind spots in real situations. Humans may be able to recognize such invisible targets based on contexts (e.g. visible human behavior and environments) around the targets, and used such recognition to predict situations in blind spots on a daily basis. As the first step towards recognizing targets in blind spots captured in videos, we propose a convolutional neural network that recognizes whether or not there is a person in a blind spot. Based on the experiments that used the volleyball dataset, which includes various interactions of players, with artificial occlusions, our proposed method achieved 90.3% accuracy in the recognition.