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This research focuses on detecting and recognizing alert traffic signs under various conditions such as illumination, scale, and pose variations. The approach involves feature design using sub-block templates to achieve high detection rates and low false positive rates. Experimental results show successful detection under extreme illumination conditions with high accuracy. The system is capable of detecting signs of various sizes and shapes, ensuring reliable performance in real-world scenarios.
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Detection & Recognition of Alert Traffic Signs Chia-Hsiung (Eric) Chen Marcus Chen Tianshi Gao
Problem Statement Detection & recognition of alert traffic signs under different illumination, scale, and pose conditions
Approach: Feature Design Sub-Block Template Total features = 14 x 15 x 8 = 1680
Experimental Result • Smallest detectable size • Stop: 20x20 • Yield: 14x14 • No left turn: 14x14 • Do no enter: 20x20 • Processing time for 640x480 image: ~30sec • Detect signs under diff/extreme illumination cond. • Scale, camera, pose invariant(< 30 degree) • Detection rate > 95%, FP < 0.1% • Based on our current test set
