Moving Object Detection Using the Genetic Algorithm for Real Times Transportation
R. Aruna Jyothi1, K. Ramesh Babu2, Srinivas Bachu3
1R. Aruna Jyothi, Department of ECE, Sai Tirumala NVR Engineering College, & Marri Laxman Reddy Institute of Technology and Management, Dundigal, Hyderabad, India.
2K. Ramesh Babu, Department of ECE, Sai Tirumala NVR Engineering College, Jonnalagadda, Gunutur, Andhra Pradesh, India.
3Srinivas Bachu, Department of ECE, Marri Laxman Reddy Institute of Technology and Management, Dundigal, Hyderabad, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 991-996 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8266088619/2019©BEIESP | DOI: 10.35940/ijeat.F8266.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Video surveillance, most commonly referred to as closed-circuit TV, is an industry over 30 years old and with a share of modifications in technology. To meet the requirements include: Better image quality, Reduction in costs, Size and scalability etc., video surveillance has experienced a number of technology shifts. For real-time traffic monitoring apps, we implement a process for the identification of objects. The suggested technique is a mixture of a GDSM, an enhanced version of the dynamic saliency map (DSM) and background subtraction. The experimental findings demonstrate the effective detection of moving objects by the suggested technique. Recent advances in vision technologies like distributed intelligent cameras have motivated scientists to create sophisticated apps for computer vision appropriate for embedded platforms. Simple and effective computer vision algorithms are needed in the integrated monitoring system with limited memory and computing resources.
Keywords: Traffic surveillance, Genetic algorithm, Dynamic saliency map.