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Efficient coarser-to-fine holistic traffic sign detection for occlusion handling

Title
Efficient coarser-to-fine holistic traffic sign detection for occlusion handling
Author
신현철
Keywords
probability; feature extraction; image recognition; object detection; learning (artificial intelligence); traffic engineering computing; image colour analysis; 100% detection results; German Traffic Sign Detection Benchmark; previous state-of-the-art methods; Korean Traffic Sign Detection dataset; efficient coarser-to-fine holistic traffic sign detection; discriminative patches; upgrades d-patches; vocabulary learning features; robust occlusion handling; holistic classifier; higher confidence-score; traffic signs; lower confidence-score; coarser-to-fine approach; traffic sign detection process; object detection; traffic sign presence; selected high probability regions
Issue Date
2018-12
Publisher
INST ENGINEERING TECHNOLOGY-IET
Citation
IET IMAGE PROCESSING, v. 12, No. 12, Page. 2229-2237
Abstract
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.
URI
https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5424http://repository.hanyang.ac.kr/handle/20.500.11754/121500
ISSN
1751-9659; 1751-9667
DOI
10.1049/iet-ipr.2018.5424
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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