Moving Object Detection for Real-Time Traffic Surveillance using Genetic Algorithm
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : Bachu Srnivas, M. Shivaranjani, N. Udaykumar, N. Anuroop Reddy
|DOI : 10.14445/22315381/IJETT-V49P259|
Bachu Srnivas, M. Shivaranjani, N. Udaykumar, N. Anuroop Reddy "Moving Object Detection for Real-Time Traffic Surveillance using Genetic Algorithm", International Journal of Engineering Trends and Technology (IJETT), V49(6),388-393 July 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Video surveillance, most commonly called CCTV (Closed-circuit television), is an industry that is more than 30years old and one that has had its share of technology changes. To meet the requirements include: Better image quality, Reduction in costs, Size and scalability etc., video surveillance has experienced a number of technology shifts. We present a moving object detection method for real-time traffic surveillance applications. The proposed method is a combination of a genetic dynamic saliency map (GDSM), which is an improved version of dynamic saliency map (DSM) and background subtraction. The experimental results show the effectiveness of the proposed method in detecting moving objects. Recent developments in vision systems such as distributed smart cameras have encouraged researchers to develop advanced computer vision applications suitable to embedded platforms. In the embedded surveillance system, where memory and computing resources are limited, simple and efficient computer vision algorithms are required.
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Traffic surveillance, Genetic algorithm, Dynamic saliency map.