Detecting and counting vehicles using adaptive background subtraction and morphological operators in real time systems
Abstract
Video surveillance is a process of evaluating the sequence of videos and is an important topic in the machine vision. The usual approach is to implement a background subtraction that takes moving objects from a part of the video frame that is significantly different from the background. Many challenges exist in the development of a background subtraction algorithm. First, it must be strong in relation to the variations in brightness intensity. Another is the identification of moving components in the background, such as shaking leaves, rain, Snow and shadows created by passing cars. Finally, the internal background model versus changes in Backgrounds such as turning off and shutting down vehicles quickly react. Therefore, we have explored this method and presented a way to update the background in this article to make this background update more effective than lighting or climate changes. In this process, morphological operators have also been used to eliminate image noises. Show that our proposed method achieves appropriate performance for both detection and counting in dynamic background.
References
[2] 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.
[3] Surendra Gupte, Osama Masoud, Robert F. K. Martin, and Nikolaos P. Papanikolopoulos “Detection and Classification of Vehicles,” IEEE P 37-47 2002.
[4] Rama Road, Rajchatavee,” Vehicle Detection and Counting from a Video Frame,” IEEE P 356-361 Bangkok 2008.
[5] Rita Cucchiara, Massimo Piccardi, Paola Mello” Image Analysis And Rule-Based Reasoning For A Traffic Monitoring System,” IEEE P 119-130 2013
[6] Harini Veeraraghavan, Osama Masoud, Nikolaos P. Papanikolopoulos”ComputerVision Algorithms for Intersection Monitoring,” IEEE P78-89, 2007.
[7] Rashid M E, Vinu Thomas, “A Background Foreground Competitive Model for Background Subtraction in Dynamic Background,” Elsevier, Procedia Technology 25, 536 – 543, 2016.
[8] T.Mahalingam, M.Subramoniam“A Robust single and multiple moving object detection tracking and classification,” Elsevier Applied Computing and Informatics, p1-10January 2018.
[9] 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.
[10] 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
[11] 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
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
