Open AccessD‐patches: effective traffic sign detection with occlusion handling
Author(s) -
Rehman Yawar,
Riaz Irfan,
Fan Xue,
Shin Hyunchul
Publication year - 2017
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2016.0303
Subject(s) - computer science , discriminative model , artificial intelligence , benchmark (surveying) , object detection , detector , traffic sign recognition , pattern recognition (psychology) , computer vision , traffic sign , sign (mathematics) , mathematics , telecommunications , mathematical analysis , geodesy , geography
In advanced driver assistance systems, accurate detection of traffic signs plays an important role in extracting information about the road ahead. However, traffic signs are persistently occluded by vehicles, trees, and other structures on road. Performance of a detector decreases drastically when occlusions are encountered especially when it is trained using full object templates. Therefore, we propose a new method called discriminative patches (d‐patches), which is a traffic sign detection (TSD) framework with occlusion handling capability. D‐patches are those regions of an object that possess the most discriminative features than their surroundings. They are mined during training and are used for classification instead of the full object templates. Furthermore, we observe that the distribution of redundant‐detections around a true‐positive is different from that around a false‐positive. Based on this observation, we propose a novel hypothesis generation scheme that uses a voting and penalisation mechanism to accurately select a true‐positive candidate. We also introduce a new Korean TSD (KTSD) dataset with several evaluation settings to facilitate detector's evaluation under different conditions. The proposed method achieves 100% detection accuracy on German TSD benchmark and achieves 4.0% better detection accuracy, when compared with other well‐known methods (under partially occluded settings), on KTSD dataset.