Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems

Maizatul Najihah Arriffin - Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
Salama A. Mostafa - Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
Umar Farooq Khattak - UNITAR International University, kelana jaya, Petaling Jaya, Selangor, Malaysia
Mustafa Musa Jaber - Dijlah University College, Baghdad, Iraq
Zirawani Baharum - Universiti Kuala Lumpur, Bandar Seri Alam, Johor Bahru, Malaysia
- Defni - Department of Information Technology, Politeknik Negeri Padang, Sumatera Barat, Indonesia
Taufik Gusman - Department of Information Technology, Politeknik Negeri Padang, Sumatera Barat, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.7.2.1820

Abstract


Image and video processing have been widely used to provide traffic parameters, which will be used to improve certain areas of traffic operations. This research aims to develop a model for estimating vehicle speed from video streams to support traffic flow analysis (TFA) systems. Subsequently, this paper proposes a vehicle speed estimation model with three main stages of achieving speed estimation: (1) pre-processing, (2) segmentation, and (3) speed detection. The model uses a bilateral filter in the pre-processing strategy to provide free-shadow image quality and sharpen the image. Gaussian filter and active contour are used to detect and track objects of interest in the image. The Pinhole model is used to assess the real distance of the item within the image sequence for speed estimation. Kalman filter and optical flow are used to flatten vehicle speed and acceleration uncertainties. This model is evaluated with a dataset that consists of video recordings of moving vehicles at traffic light junctions on the urban roadway. The average percentage for speed estimation error is 20.86%. The average percentage for accuracy obtained is 79.14%, and the overall average precision of 0.08.

Keywords


Traffic flow analysis; vehicle speed estimation; Kalman filter; Pinhole model; bilateral filter

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References


J. L. Zambrano-Martinez, C. T. Calafate, D. Soler, J. C. Cano, and P. Manzoni, “Modeling and characterization of traffic flows in urban environments,†Sensors (Switzerland), vol. 18, no. 7, pp. 1–19, 2018.

S. A. Kashinath et al., “Review of data fusion methods for real-time and multi-sensor traffic flow analysis,†IEEE Access, vol. 9, pp. 51258–51276, 2021.

S. A. Kashinath, S. A. Mostafa, D. Lim, A. Mustapha, H. Hafit, and R. Darman, “A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources,†J. Intell. Syst., vol. 30, no. 1, pp. 947–965, 2021.

S. S. Wardha, S. M. Deokar, S. S. Patankar, and J. V. Kulkarni, “Development of automated technique for vehicle speed estimation and tracking in video stream,†RTEICT 2017 - 2nd IEEE Int. Conf. Recent Trends Electron. Inf. Commun. Technol. Proc., vol. 2018-January, pp. 940–944, 2017.

J. Sochor et al., “Comprehensive Data Set for Automatic Single Camera Visual Speed Measurement,†IEEE Trans. Intell. Transp. Syst., vol. 20, no. 5, pp. 1633–1643, 2019.

D. C. Luvizon, B. T. Nassu, and R. Minetto, “A Video-Based System for Vehicle Speed Measurement in Urban Roadways,†IEEE Trans. Intell. Transp. Syst., vol. 18, no. 6, pp. 1393–1404, 2017.

I. Sina, A. Wibisono, A. Nurhadiyatna, B. Hardjono, W. Jatmiko, and P. Mursanto, “Vehicle counting and speed measurement using headlight detection,†2013 Int. Conf. Adv. Comput. Sci. Inf. Syst. ICACSIS 2013, no. April 2015, pp. 149–154, 2013.

J. Gerat, D. Sopiak, M. Oravec, and J. Pavlovicova, “Vehicle speed detection from camera stream using image processing methods,†Proc. Elmar - Int. Symp. Electron. Mar., vol. 2017-September, no. September, pp. 201–204, 2017.

L. Zhang, Y. Xie, L. Xidao, and X. Zhang, “Multi-source heterogeneous data fusion,†2018 Int. Conf. Artif. Intell. Big Data, ICAIBD 2018, pp. 47–51, 2018.

K. Dilpreet and K. Yadwinder, “Various Image Segmentation Techniques: A Review,†Int. J. Comput. Sci. Mob. Comput., vol. 3, no. 5, pp. 809–814, 2014.

R. Ke, S. Kim, Z. Li, and Y. Wang, “Motion-vector clustering for traffic speed detection from UAV video,†2015 IEEE 1st Int. Smart Cities Conf. ISC2 2015, no. October, 2015.

N. A. Mohd, S. A. Mostafa, A. Mustapha, A. A. Ramli, M. A. Mohammed, and N. M. Kumar, “Vehicles counting from video stream for automatic traffic flow analysis systems,†Int. J. Emerg. Trends Eng. Res., vol. 8, no. 1 Special Issue 1, pp. 142–146, 2020.

J. Al-Azzeh, B. Zahran, and Z. Alqadi, “Salt and pepper noise: Effects and removal,†Int. J. Informatics Vis., vol. 2, no. 4, pp. 252–256, 2018.

C. Yi and J. Cho, “Real-time Estimation of Road Surfaces using Fast Monocular Depth Estimation and Normal Vector Clustering,†vol. 5, no. September, pp. 206–211, 2021.

M. A. Aljamal, H. M. Abdelghaffar, and H. A. Rakha, “Developing a neural–kalman filtering approach for estimating traffic stream density using probe vehicle data,†Sensors (Switzerland), vol. 19, no. 19, pp. 1–18, 2019.

R. Krishnapuram, S. Shorewala, and P. Rao, “Link Speed Estimation for Traffic Flow Modelling Based on Video Feeds from Monocular Cameras,†2020 IEEE 23rd Int. Conf. Intell. Transp. Syst. ITSC 2020, pp. 1–6, 2020.

D. Biswas, H. Su, C. Wang, and A. Stevanovic, “Speed estimation of multiple moving objects from a moving UAV platform,†ISPRS Int. J. Geo-Information, vol. 8, no. 6, 2019.

Z. Czapla, “S Pa 2017 Vehicle Speed Estimation with the Use of Gradient- Based Image Conversion into Binary Form,†pp. 213–216, 2017.

T. V. Mini and T. Vijayakumar, “Speed estimation and detection of moving vehicles based on probabilistic principal component analysis and new digital image processing approach,†in AI International Conference on Big Data Innovation for Sustainable Cognitive Computing: BDCC 2018, pp. 221–230.

A. Raj et al., “Semi-Geometrical approach to estimate the speed of the vehicle through a surveillance video stream,†Int. J. Comput. Sci. Eng., vol. 7, no. 3, pp. 741–748, 2019.

S. Hua, M. Kapoor, and D. C. Anastasiu, “Vehicle tracking and speed estimation from traffic videos,†IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2018-June, pp. 153–160, 2018.

H. K. Gauttam and R. K. Mohapatra, “Speed prediction of fast approaching vehicle using moving camera,†Commun. Comput. Inf. Sci., vol. 1148 CCIS, pp. 423–431, 2020.

S. A. Mostafa, M. S. Ahmad, M. Annamalai, A. Ahmad, and S. S. Gunasekaran, “A conceptual model of layered adjustable autonomy,†In Advances in information systems and technologies (pp. 619-630). Springer, Berlin, Heidelberg, 2013.

S. A. Mostafa, S. S. Gunasekaran, A. Mustapha, M. A, Mohammed, and W. M. Abduallah, “Modelling an adjustable autonomous multi-agent internet of things system for elderly smart home,†In International Conference on Applied Human Factors and Ergonomics (pp. 301-311). Springer, Cham, 2019.