Kernel-based tracking for improving sign detection performance

To be deployed in the real-world, automatic and semi-automatic systems should understand traffic rules by recognizing and comprehending contents of traffic signs, because traffic signs inform what driving behaviors should be. In this paper, we present the successful application of methods to improve the traffic sign localization performance. Given a potential sign region, our algorithm represents both the detected sign as a target and candidates in the subsequent frame as probability density functions. Then, our algorithm maximizes the similarity between a target and candidates to localize the sign. Finally, the maximum similarity among candidates is assigned as a tracked sign. The experimental results verify that our algorithm can robustly localize traffic signs in images under various weather conditions and driving scenarios.