A Cognitively Motivated Method for Classification of Occluded Traffic Signs
受人类识别遮挡标志的认知过程启发，提出一种新结构显式处理部分遮挡的交通标志样本，通过分析遮挡图并设计遮挡描述子来区分遮挡标志与负样本，减少漏检，可用于其他目标检测。
Classification of traffic signs with partial occlusions is important for traffic sign maintenance and inventory systems. It is also important to help drivers identify possible traffic signs in time. Motivated by human cognitive processes in identifying an occluded sign, a novel structure is designed to explicitly handle occluded samples in this paper. Occlusion maps are analyzed for possible occluded signs, and a new occlusion descriptor is proposed to distinguish occluded signs from negative samples. A series of tests shows that the developed method could effectively handle samples with partial occlusions and thus reduce the missed detections caused by occlusions. The developed method could also be easily used for any other object detection.