A Cognitively Motivated Method for Classification of Occluded Traffic Signs

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...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2017-02, Vol.47 (2), p.255-262
Main Authors: Hou, Ya-Li, Hao, Xiaoli, Chen, Houjin
Format: Article
Language:English
Subjects:
Cascaded support vector machine (SVM) classifiers
Classification
Cognitive tasks
Feature extraction
Image color analysis
Lighting
Occluded object recognition
Occlusion
partial occlusions
Robustness
Shape
Support vector machines
traffic sign classification
Traffic signs
Vehicles
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Summary: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.
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2016.2560126