A Real Time Fingers Detection by Symmetry Transform Using a Two Cameras System
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Pattern Recognition
A Real-Time Road Sign Detection Using Bilateral Chinese Transform
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Mitigation of visibility loss for advanced camera-based driver assistance
IEEE Transactions on Intelligent Transportation Systems
Goal evaluation of segmentation algorithms for traffic sign recognition
IEEE Transactions on Intelligent Transportation Systems
Real-time traffic sign detection with vehicle camera images
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
Real-time traffic sign recognition in three stages
Robotics and Autonomous Systems
Efficient algorithm for automatic road sign recognition and its hardware implementation
Journal of Real-Time Image Processing
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Algorithms for classifying road signs have a high computational cost per pixel processed. A detection stage that has a lower computational cost can facilitate real-time processing. Various authors have used shape and color-based detectors. Shape-based detectors have an advantage under variable lighting conditions and sign deterioration that, although the apparent color may change, the shape is preserved. In this paper, we present the radial symmetry detector for detecting speed signs. We evaluate the detector itself in a system that is mounted within a road vehicle. We also evaluate its performance that is integrated with classification over a series of sequences from roads around Canberra and demonstrate it while running online in our road vehicle. We show that it can detect signs with high reliability in real time. We examine the internal parameters of the algorithm to adapt it to road sign detection. We demonstrate the stability of the system under the variation of these parameters and show computational speed gains through their tuning. The detector is demonstrated to work under a wide variety of visual conditions.