![]() Chen, Recent advances in convolutional neural networks. Shahbahrami, Vehicle counting method based on digital image processing algorithms, in 2015 Second International Conference on Pattern Recognition and Image Analysis (IPRIA), (IEEE, Piscataway, NJ, 2015), pp. Banerjee, Multiple kernel based KNN classifiers for vehicle classification. Salgado, A study of feature combination for vehicle detection based on image processing. Fang, A rapid learning algorithm for vehicle classification. Velastin, Vehicle detection, tracking and classification in urban traffic, in 2012 15th International IEEE Conference on Intelligent Transportation Systems, 16 Sept 2012 (IEEE, Piscataway, NJ, 2012), pp. Akbarzadeh-T, Vehicle recognition based on Fourier, wavelet and curvelet transforms-a comparative study, in Fourth International Conference on Information Technology (ITNG’07), (IEEE, Piscataway, NJ, 2007), pp. Nieto, Robust vehicle detection through multidimensional classification for on board video-based systems, in 2007 IEEE International Conference on Image Processing, 16 Sept 2007, vol. ![]() Cho, Online boosting for vehicle detection. Hsiao, Car make and model recognition using 3d curve alignment, in IEEE Winter Conference on Applications of Computer Vision, (IEEE, Piscataway, NJ, 2014), pp. ![]() Riaz, Bayesian prior models for vehicle make and model recognition, in Proceedings of the 7th International Conference on Frontiers of Information Technology, (2009, Dec 16), pp. Xia, Vehicle detection from 3D lidar using fully convolutional network. Shi, Vehicle detection and classification by measuring and processing magnetic signal. Nakamiya, Robust vehicle detection under various environments to realize road traffic flow surveillance using an infrared thermal camera. Jung, Analysis of vehicle detection with WSN-based ultrasonic sensors. Rediscovering Measurement in the Age of Informatics (Cat. Kompa, Applications using a low-cost baseband pulsed microwave radar sensor, in IMTC 2001: Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Mary, Exploring sound signature for vehicle detection and classification using ANN. Mumtaz, Deep unified model for face recognition based on convolution neural network and edge computing. Al-Turjman, QoS-aware data delivery framework for safety-inspired multimedia in integrated vehicular-IoT. Al-Turjman, Cognitive routing protocol for disaster-inspired internet of things. Proposed system classifies the vehicles with the average accuracy of 96.2% on six types of vehicles. Proposed system first detects the vehicle and then classifies its brand type accurately on self-generated dataset with the help of UAVs. In this chapter we present a deep learning-based approach which involves two phases: region proposal network and classification network both networks use the features from convolution neural network (CNN). ![]() With the advancement in technology, deep learning has inspired us all with its autonomous feature extraction and accuracy. However, they failed to provide the promising results because of limited viewpoints and lack of dataset. Traditional approaches like (Bayesian prior models, scale-invariant feature transform, speeded-up robust features, and support vector machine) image processing and machine learning are used by many researchers to automate the transportation system. Classification of vehicle type is the major component of intelligent transportation system. This excessive use of the unmanned aerial vehicles also demands the devices and the deployed system that devices are fully autonomous, efficient, and accurate. These applications include the surveillance of the crowd, transport, and disaster management and the inspection of the building architecture. Unmanned aerial vehicles have been utilized in various applications for the management of the civil and defense infrastructure. ![]()
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