Open AccessEfficient coarser‐to‐fine holistic traffic sign detection for occlusion handling
Author(s) -
Rehman Yawar,
Ahmed Khan Jameel,
Shin Hyunchul
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5424
Subject(s) - computer science , traffic sign recognition , artificial intelligence , discriminative model , traffic sign , object detection , benchmark (surveying) , sliding window protocol , classifier (uml) , pattern recognition (psychology) , sign (mathematics) , speedup , computer vision , window (computing) , mathematics , geodesy , geography , operating system , mathematical analysis
In this study, the authors present a new efficient method based on discriminative patches (d‐patches) for holistic traffic sign detection with occlusion handling. Traffic sign detection is an important part in autonomous driving, but usually hampered by the occlusions encountered on roads. They propose a method which basically upgrades d‐patches by integrating vocabulary learning features. Consequently, d‐patches are more discriminatively trained for robust occlusion handling. In addition, a holistic classifier is trained on d‐patches, which identify those regions where occlusion exists. This results in higher confidence‐score for the regions which contain traffic signs and lower confidence‐score for the regions containing occlusions. Furthermore, they also propose a new coarser‐to‐fine (CTF) approach to speed up the traffic sign detection process. CTF minimises the use of traditional sliding window for object detection. It relies on colour variance to search the regions with high probability of traffic sign presence. Sliding window is used only on the selected high probability regions. The proposed method achieves 100% detection results on German Traffic Sign Detection Benchmark and performs 2.2% better than the previous state‐of‐the‐art methods on Korean Traffic Sign Detection dataset, under partially occluded settings. By using CTF approach, five times speedup with a marginal loss in accuracy can be achieved.