A Research on Constructing the Recognition System for the Dynamic Pedestrian Traffic Signals Through Machine Vision
In order to assist people with visually degraded or damaged to obtain the information from the dynamic pedestrian traffic signals at the intersection, this system captured the image in front of the pedestrian through the camera, and analyzed the content of the image to recognize the dynamic pedestrian traffic signal (traffic lights with the countdown seconds) at the intersection. Through the Haar-like with HSV color correction in the experiment, the average accuracy of the recognition result was 94.6%, and the average recall rate was 81.6%. In addition, as for the digit identification of the countdown seconds, the coordinates of the seconds were estimated first, the digit image was centered and then divided. Finally, the digit recognition was performed and then converted back to the number of seconds. The average precision of the recognition result was 95.2%, and the average recall rate was 77.2%.
The completion of this paper is very grateful to the Ministry of Science and Technology MOST 107-2221-E-218-023-MY2.
- 1.Wu, C.C., Hsu, Y.C.: The case study of capturing images for the dynamic pedestrian traffic sign. In: 2017 International Symposium on Novel and Sustainable Technology, pp. 84–85 (2017)Google Scholar
- 2.Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
- 3.Shanmugavadivu, P., Ashish, K.: Human skin detection in digital images using multi colour scheme system. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1–6 (2017)Google Scholar
- 4.Ranjit, G., Ayan, B.: An improved scene text and document image binarization scheme. In: 2018 4th International Conference on Recent Advances in Information Technology (RAIT), pp. 1–6 (2018)Google Scholar
- 5.Jin, H., Sun, H.: Rendering fake soft shadows based on the erosion and dilation. In: 2010 2nd International Conference on Computer Engineering and Technology, vol. 6, p. 236 (2010)Google Scholar
- 6.Meng, L.Y., Zaiqing, C., Feiyan, C.: Research on video face detection based on AdaBoost algorithm training classifier. In: 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS), pp. 1–6 (2017)Google Scholar