Classification of Vehicle Make Based on Geometric Features and Appearance-Based Attributes Under Complex Background
subject: Auto correlogram; Color moments; Texture extraction
description: Vehicle detection and recognition is an important task in the area of advanced infrastructure and movement administration. Many researchers are working on this area with different approaches to solve the problem since it has a many challenge. Every vehicle has its on own unique features for recognition. This paper focus on identifying the vehicle brand based on its geometrical features and diverse appearance-based attributes like colour, occlusion, shadow and illumination. These attributes will make the problem very challenging. In the proposed work, system will be trained with different samples of vehicles belongs to the different make. Classify those samples into different classes of models belongs to same make using Neural Network Classifier. Exploratory outcomes display promising possibilities efficiently. 2019, Springer Nature Singapore Pte Ltd.
creator: Arunkumar K.L.; Danti A.; Manjunatha H.T.
source: Communications in Computer and Information Science, Vol-1035, pp. 41-48.
publisher: Springer Verlag
date: 2019-01-01
rights: Restricted Access
relation: ISSN: 18650929; ISBN: 978-981139180-4
format: Online
language: English
type: Conference paper
coverage: Arunkumar K.L., Department of MCA, Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India; Danti A., Computer Science and Engineering, Christ University, Bangalore, Karnataka, India; Manjunatha H.T., Department of MCA, Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India