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Publication Type : Conference Paper
Publisher : 2018 5th International Conference on Signal Processing and Integrated Networks, SPIN 2018, Institute of Electrical and Electronics Engineers Inc.
Source : 2018 5th International Conference on Signal Processing and Integrated Networks, SPIN 2018, Institute of Electrical and Electronics Engineers Inc. (2018)
ISBN : 9781538630457
Keywords : Aerial photography, Antennas, Classification (of information), Classification accuracy, Decision trees, Deep neural networks, extraction, Extraction capability, Feature extraction, Feature extraction techniques, Feature extractor, Histogram of oriented gradients, image classification, Learning algorithms, Military applications, Military photography, Military vehicles, Neural networks, Singular value decomposition, Standard machines, Standard neural network models, Traffic monitoring
Campus : Coimbatore
School : Computational Engineering and Networking
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN)
Verified : No
Year : 2018
Abstract : Detection of vehicles from aerial images have several real world implications in surveillance, military applications, traffic lot management, border patrol and traffic monitoring. The system proposed in this paper intends to automate the process of detecting vehicles from aerial images, rather than relying on a human operator. Here, we identify an optimum classification strategy for the proposed detection system, which is the initial stage of designing a vehicle detection pipeline. This research focuses on the feature extraction capabilities of standard neural network models like, Alexnet  and VGG-16 , which are compared against classic feature extraction techniques, like Histogram of Oriented Gradients and Singular Value Decomposition. The extracted features are benchmarked across standard machine learning algorithms such as Support Vector Machine and random forest. It is observed that the neural net extracted features gives an overall classification accuracy of 99% on the VEDAI dataset. The classification was treated as a binary class problem with vehicles as one class and rest everything as non-vehicles. © 2018 IEEE.
Cite this Research Publication : V. S. Mohan, Sowmya, and Dr. Soman K. P., “Deep Neural Networks as Feature Extractors for Classification of Vehicles in Aerial Imagery”, in 2018 5th International Conference on Signal Processing and Integrated Networks, SPIN 2018, 2018.