Publication Type : Conference Paper
Thematic Areas : Wireless Network and Application
Publisher : 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) .
Source : 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (2018)
Keywords : Autonomous navigation, Bagging, Classification algorithms, CNN algorithms, CNN architecture, convolutional neural nets, Convolutional neural networks, deep CNN, Deep learning, flood depth estimation, floods, image classification, Law enforcement, learning (artificial intelligence), Machine learning algorithms, Object Detection, Object recognition, partially submerged vehicles, RCNN, Road vehicles, spatial variables measurement, traffic engineering computing, Traffic surveillance, transportation management, vehicle classification, vehicle model detection, vehicle type detection.
Campus : Amritapuri
School : School of Engineering
Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)
Department : Wireless Networks and Applications (AWNA)
Year : 2018
Abstract : Become a popular field of study. We plan to use vehicle classification for estimation of flood depth using images with partially submerged vehicles. The aim of this paper is to provide researchers with an overview of different machine learning algorithms used for vehicle classification using Convolutional Neural Networks (CNN) architecture. Different CNN algorithms have been used for classification of vehicles efficiently. This work reviews and compares the various classification algorithms for detecting the type as well as the model of a vehicle.
Cite this Research Publication : A. Murali, Nair, B. B., and Rao, S. N., “Comparative Study of Different CNNs for Vehicle Classification”, in 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2018.