Detecting and counting vehicles using adaptive background subtraction and morphological operators in real time systems
الموضوعات :Lida Shahmiri 1 , Sajad Tavassoli 2 , Seyed Navid Hejazi Jouybari 3
1 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
2 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
3 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
الکلمات المفتاحية: background subtraction, morphological operators, machine vision, image processing, vehicle detection,
ملخص المقالة :
vehicle detection and classification of vehicles play an important role in decision making for the purpose of traffic control and management.this paper presents novel approach of automating detecting and counting vehicles for traffic monitoring through the usage of background subtraction and morphological operators. We present adaptive background subtraction that is compatible with weather and lighting changes. Among the various challenges involved in the background modeling process, the challenge of overcoming lighting scene changes and dynamic background modeling are the most important issues. The basic architecture of our approach is done in 3 steps: 1-background subtraction 2- segmentation module 3- detection of objects and counting vehicles. We present an adaptive background at each frame after using binary motion mask to create instantaneous image of background. To remove noises we use morphological operators and then start to segment images, detect vehicles and count them. Algorithm is efficient and able to run in real-time. Some experimental results and conclusions are presented
[1] Benny Hardjono, Mario G.A.Rhizma, Andree E. Widjaj,” Vehicle Counting Quantitative Comparison Using Background Subtraction, Viola Jones and Deep Learning Methods Benny” IEEE 2018.
[2] G.Salvi, “An Automated Nighttime Vehicle Counting and Detection System for Traffic Surveillance,” IEEE International Conference on Computational Science and Computational Intelligence.P1-6 2014.
[3] Zi Yang, Lilian S.C. Pun-Cheng “Vehicle detection in intelligent transportation systems and its applications under varying environments: A review,” Elsevier the Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, p143-154, October 2017.
[4] Sheng-Yi Chiu , Chung-Cheng Chiu ,Sendren Sheng-Dong Xu 2” A Background Subtraction Algorithm in Complex Environments Based on Category Entropy Analysis” Appl. Sci. 2018.
[5] Nursuriati Jamil, Tengku Mohd Tengku Sembok, Zainab Abu Bakar ”Noise Removal and Enhancement of Binary Images Using Morphological Operations “IEEE 2008.
[6] Miss Helly M Desai, Mr. Vaibhav Gandhi,”A Survey: Background Subtraction Techniques” International Journal of Scientific & Engineering Research, Volume 5, Issue 12, December-2014.
[7] Ssu-Wei Chen, Luke K. Wang, Jen-Hong Lan” Moving Object tracking Based on Background Subtraction Combined Temporal Difference” International Conference on Emerging Trends in Computer and Image Processing (ICETCIP') Dec.2011.
[8] Yaochi Zhao, Zhuhua Hu,Xiong Yang, and Yong Bai “Moving Object Detection Method with Temporal and Spatial Variation Based on Multi-info Fusion “Springer International Publishing Switzerland 2014.
[9] A. Pal, G. Schaefer and M. Celebi, "Robust Codebook-based Video Background Subtraction," in IEEE, 2010.
[10] K. Chang, P. Liu and Y. Wang, "Parallel design of Background subtraction and Template Matching modules for Image Objects Tracking System," in Computer Symposium, 2016.
[11] H. Lee, H. Kim and J. Kim, "Background Subtraction using background sets with Image and Color Space reduction," in IEEE Transaction on Multimedia, 2016.
[12] Aisha Ajmal and Ibrahim M. Hussain “Morphological Process based Vehicle Detection and Classification “MASAUM Journal of Basic and Applied Sciences Vol.1,No. 2 September 2009 .
[13] EmployerNTNU - Norwegian University of Science and Technology” Vehicle Detection and Tracking using the Optical Flow and Background Subtraction” Elsevier, 2013.
[14] Neha S. Sakpal, Manoj Sabnis “Adaptive Background Subtraction in Images”IEEE 2018.
[15] Arun Varghese, Sreelekha G” Background Subtraction for Vehicle Detection” Global Conference on Communication Technologies IEEE2015
[16] Rama Road, Rajchatavee,” Vehicle Detection and Counting from a Video Frame,” IEEE P 356-361 Bangkok 2008.
[17] Rita Cucchiara, Massimo Piccardi, Paola Mello” Image Analysis And Rule-Based Reasoning For A Traffic Monitoring System,” IEEE P 119-130 2013.
[18] Harini Veeraraghavan, Osama Masoud, Nikolaos P. Papanikolopoulos”ComputerVision Algorithms for Intersection Monitoring,” IEEE P78-89, 2007.
[19] Rashid M E, Vinu Thomas, “A Background Foreground Competitive Model for Background Subtraction in Dynamic Background,” Elsevier, Procedia Technology 25, 536 – 543, 2016.
[20] T.Mahalingam, M.Subramoniam“A Robust single and multiple moving object detection tracking and classification,” Elsevier Applied Computing and Informatics, p1-10January 2018.
[21] MD. Hazrat ALI A, Syuhei Kurokawab, A. Shafiec, “Autonomous Road Surveillance System: A Proposed Model for Vehicle Detection and Traffic Signal Control,” ELSEVIER P 963-970, 2013.
[22] N.Senthilkumaran, J.Thimmiaraja”An Illustrative Analysis of Mathematical Morphology Operations for MRI Brain Images “Department of Computer Science and Applications, International Journal of Computer Science and Information Technologies, Vol. 5 (3), 2684-2688 2014.
[23] S. Arora, J. Acharya, A. Verma, Prasanta K. Panigrahi” Multilevel thresholding for image segmentation through a fast statistical recursive algorithm” Pattern Recognition Letters 29 119–125, 2008.
