Detection-by-tracking of traffic signs in videos

Yanting Zhang, Zijian Wang, Ruoning Song, Cairong Yan, Yonggang Qi

Abstract: Continuously detecting traffic signs in a video sequence is necessary for autonomous or assisted driving scenarios, since a vehicle needs the information from the signs to facilitate navigation. Single-image based traffic sign detector may fail in many cases, when the car moves fast on the road, resulting in motion blur, partial occlusion, and abrupt environmental change. In this paper, we propose an effective methodology, called detection-by-tracking, for robust traffic sign detection in videos, so as to improve the detection performance beyond a basic object detector. We explore the temporal cues among frames to help with the proposal reasoning for further regression. The correlations of spatial location and appearance similarity for the same sign in adjacent frames are considered in our approach. Experimental results show that the proposed detection-by-tracking mechanism is helpful, with improved detection performance to a large extent. Moreover, the idea of the detection-by-tracking can also be generalized to other scenarios for object detection tasks in videos.