Improved Traffic Sign Detection in Videos through Reasoning Effective RoI Proposals

Traffic sign detection is an important task in assisted safety and autonomous driving. It is important to continuously detect the traffic signs emerged on the road. Currently, most object detection methods make independent detections based on single images. When we apply these methods directly to a video clip to detect traffic signs without taking into account temporal correlations among adjacent frames, missed detections or incorrect detections can frequently occur due to motion blur, size change, partial occlusion, and/or bad pose. In this paper, we fully exploit the temporal consistency of traffic sign detection in videos. More specifically, we incorporate information of adjacent frames with high confidence scores to enhance the discovery of potential objects in the missed or incorrect detected frames by "recovering" the missed RoI proposals or by "improving" the incorrect RoI proposals with low confidence scores. Our method can be regarded as a "detection-by-tracking" strategy, which results in a more robust detection performance in videos.