Online ISSN: 2515-8260

Keywords : Multi directional


VEHICLE RETRIEVAL USING SIMILARITY MEASURE CHECK FOR OPTIMAL FEATURE SELECTION

Rashmita khilar; K. Lalitha; S.Sylvia Irish

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1861-1871

Vehicle retrieval is a demanding application in interdisciplinary research areas such Vision-Based Intelligent Transportation System (ITS), finding traffic density, recognising licence plate, analysing traffic flow etc., Vehicle retrieval becomes possible by detecting and tracking the vehicles. An efficient framework for vehicle detection and tracking system to retrieve vehicle is a great demand in the field of ITS system. In this paper vehicle retrieval based on vehicle detection and tracking is developed based on selecting high level feature set like size and shape for an efficient vehicle retrieval system which is in turn helps to reduce the traffic flow on highways thereby reducing accidents happen on road, autonomous vehicle guidance, vehicle safety, helps in finding the parking slot and identifying suspicious vehicles etc. Mostly vehicle retrieval systems are query based, attribute based such as colour, shape, size etc., and licence plate based retrieval. In this paper vehicles are retrieved from various features like increasing number of vehicle on the road day-by-day, increasing number of cameras etc., Multidirectional Grey-Level Texture and Shape Model for Feature Based Vehicle Retrieval System (MDGLTS -VRS) for vehicle retrieval has been developed. This approach identifies an optimal subset of features, useful to discriminate between local and global features.