A Cognitively Motivated Method for Classification of Occluded Traffic Signs

作者：Hou, Ya-Li^*; Hao, Xiaoli; Chen, Houjin

来源：IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2017, 47(2): 255-262.

DOI：10.1109/TSMC.2016.2560126

• Cascaded support vector machine (SVM) classifiers
• partial occlusions
• traffic sign classification

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.

• 出版日期2017-2
• 单位北京交通大学