A Gaussian Filter based SVM Approach for Vehicle Class Identification

Abstract

Abstract-Vehicle identification or classification is one of the application areas that come under real time image processing. Vehicle recognition is having the significance in various applications including the traffic monitoring, load monitoring, number plate recognition, vehicle theft prevention, traffic violation detection, management of traffic etc. As the images are captured as primary data source, it can have number of associated impurities which include the background inclusion, object overlapping etc. Because of this, object detection and recognition is always a challenge in real time scenario. In present work, a robust feature based model is presented for feature extraction and classification of vehicle images. The presented model is applied on real time captured image to categorize the vehicle in light, medium and heavy vehicle. Firstly, the vehicle area segmentation is performed and later on the Gaussian filter is applied to extract the image features. This featured dataset is processed under Support Vector Machine (SVM) based distance analysis model for vehicle recognition and vehicle class identification. The experimentation results of present investigation shows the recognition rate of devised system over 90%.

BIM and Computer Vision-Based Framework for Fire Emergency Evacuation Considering Local Safety Performance

“…Yang et al (2010) [43] proposed a solution that uses cameras to locate and track multiple construction site workers through template matching, which achieves the identification and statistics of multiple workers from the same perspective, and real-time positioning. Gargi and Sandeep (2015) [44] proposed a computer vision-based vehicle monitoring method at the construction site, automatically performing statistics and monitoring of vehicles on the construction site. Fang et al (2020) [45] enhanced the monocular vision technique for the localization of construction-related entities.…”