A GPU-based real-time traffic sign detection and recognition system
Document Type
Conference Proceeding
Date of Original Version
1-13-2015
Abstract
This paper presents a GPU-based system for real-time traffic sign detection and recognition which can classify 48 different traffic signs included in the library. The proposed design implementation has three stages: pre-processing, feature extraction and classification. For high-speed processing, we propose a window-based histogram of gradient algorithm that is highly optimized for parallel processing on a GPU. For detecting signs in various sizes, the processing was applied at 32 scale levels. For more accurate recognition, multiple levels of supported vector machines are employed to classify the traffic signs. The proposed system can process 27.9 frames per second video with active pixels of 1,628 × 1,236 resolution. Evaluating using the BelgiumTS dataset, the experimental results show the detection rate is about 91.69% with false positives per window of 3.39 × 10-5 and the recognition rate is about 93.77%.
Publication Title, e.g., Journal
IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIVTS 2014: 2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems, Proceedings
Citation/Publisher Attribution
Chen, Zhilu, Xinming Huang, Zhen Ni, and Haibo He. "A GPU-based real-time traffic sign detection and recognition system." IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIVTS 2014: 2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems, Proceedings (2015): 1-5. doi: 10.1109/CIVTS.2014.7009470.