Object Bounding Box-Critic Networks for Occlusion-Robust Object Detection in Road Scene

Abstract

Object detection in a road scene has received a significant attention from research fields of developing autonomous vehicle and automatic road monitoring systems. However, object occlusion problems frequently occur in generic road scenes. Due to such occlusion problems, previous object detection methods have limitations of not being able to detect objects accurately. In this paper, we propose a novel object detection network which is robust in occlusions. For effective object detection even with occlusion, the proposed network mainly consists of two parts; 1) Object detection framework, 2) Multiple object bounding box (OBB)-Critic network for predicting a BB map which estimates both object region and occlusion region. Comprehensive experimental results on a KITTI Vision Benchmark Suite dataset showed that the proposed object detection network outperformed the state-of-the-art methods.