The week's pick
Reseach Article
Traffic Density Identification based on Neural Network and Histogram
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 172 - Number 9 |
| Year of Publication: 2017 |
| Authors: Luong Anh Tuan Nguyen, Thi-Ngoc-Thanh Nguyen |
10.5120/ijca2017915202
|
Luong Anh Tuan Nguyen, Thi-Ngoc-Thanh Nguyen . Traffic Density Identification based on Neural Network and Histogram. International Journal of Computer Applications. 172, 9 ( Aug 2017), 8-13. DOI=10.5120/ijca2017915202
Abstract
The traffic density identification will support the traffic problems such as intelligent traffic signal control, traffic planning, etc. This paper proposes a traffic density identification method based on histogram and neural network. The system model was designed and evaluated with the traffic image datasets of Ho Chi Minh city. The best identifying result can obtain 96%.
References
- Ozkurt C, Camci F. Automatic traffic density estimation and vehicle classification for traffic surveillance systems using Neural Networks. Mathematical and Computational Applications. 2009; 14(3):187–96.
- C. Stuiz, T. A. Runkler, “Classification and Predicts of Road Traffic using Application Specific Fuzzy Clustering”, Fuzzy Systems, IEEE Transactions, pp. 297-308, 2002.
- Luong Anh Tuan Nguyen, Thi-Ngoc-Thanh Nguyen. Traffic Image Classification using Horizontal Slice Algorithm. International Journal of Computer Applications (ISSN: 0975 – 8887), Volume 148 – No.11, pp. 30-34, August 2016.
- Al Bovik (2000), Handbook of Image and Video Processing, Academic Press.
- Rafael C.Gonzalez, Richard E. Woods ( 1993), Digital Image Processing, Addison Wesley Pub.Comp.
- Luong Anh Tuan Nguyen, Huu Khuong Nguyen. Traffic Density Identification Based On Histogram. Journal of Transportation Science and Technology, ISSN: 1859-4263, Vol 15-05/2015, pp 23-27.
- Xiangyun Ye, Mohamed Cheriet, Senior Member, Ching Y. Suen (2001), Stroke-Model-Based Character Extraction from Gray-Level Document Images, IEEE.
- C. C. Sun. S. J. Ruan, M. C. Shie, T. W. Pai, “Dynamic Contrast Enhancement based on Histogram Specification,” IEEE Transactions on Consumer Electronics, 51(4), pp.1300-1305, 2005.
- Jiawei Han, Micheline Kamber (2006), Data Mining: Concepts and Techniques, Second Edition, Elsevier Inc. All rights reserved.
- C. Willmott, and K. Matsuura (2005), Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in assessing average model performance, Clim. Res., 30, 79–82.
Index Terms
Keywords