A Recognition Method of Pedestrians’ Running in the Red Light Based on Image
- 1 Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- 2 Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- 3 Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
Abstract
It is dangerous for pedestrians to run when the traffic shows a red light, but in some cases the pedestrians are breaking the rules. This system will be a meaningful thing if the jaywalking behaviors of pedestrians in the road crossing through the monitoring cameras could be recognized. Then drivers can be informed of the situations in advance, and they can take some actions to avoid an accident. The characteristic behavior is the non-construction, and furthermore, due to the change of sunlight, temperature, and weather in the outside environment, and the shaking of cameras themselves, the background images will change as time goes by, which will bring special difficulties in recognizing jaywalking behaviors. In this paper, the method of adaptive background model of mixture Gaussian is used to extract the moving objects in the video. On the base of Histograms of Oriented Gradients (HOG), the pedestrians images and car images from MIT Library are used to train our monitoring system by SVM classifier, and identify the pedestrians in the video. Then, the color histogram, position information and the movement of pedestrians are selected to track them. After that we can identify whether the pedestrians are running in the red lights or not, according to the transportation signals and allocated walking areas. The experiments are implemented to show that the proposed method is effective.
- Stauffer, C. and Grimson, W.E.L. (1999) Adaptive Background Mixture Models for Real-Time Tracking. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, 23-25 June 1999.
- Wohler, C., Kressel, U. and Anlaur, J.K. (2000) Pedestrian Recognition by Classification of Image Sequences Global Approaches vs. Local Spatio-Temporal Processing. 15th International Conference on Pattern Recognition, Barcelona, 3-7 September 2000, 540-544.
- Gavrila, D.M. (2000) Pedestrian Detection from a Moving Vehicle. In: Vernon, D., Ed., Computer Vision—ECCV 2000, Springer, Berlin, 37-49. http://dx.doi.org/10.1007/3-540-45053-X_3
- Dalal, N. and Triggs, B. (2005) Histograms of Oriented Gradients for Human Detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 25-25 June 2005, 886-893.
- Chang, F.L., Ma, L. and Qiao, Y.Z. (2007) Human Oriented Multi-Target Tracking Algorithm in Video Sequence. Control and Decision, 22, 418-422.
- Liu, G.C. and Wang, Y.J. (2009) An Algorithm of Muli-Target Tracking Based on Improved Particle Filter. Control and Decision, 22, 317-320.
- Takala, V. and Pietikainen, M. (2007) Multi-Object Tracking Using Color, Texture and Motion. IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, 17-22 June 2007, 1-7.
- Yang, T., Pan, Q. and Li, J. (2005) Real-Time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes. IEEE Conference on Computer Vision and Pattern Recognition, 20-25 June 2005, 970-975.