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Title	Deep Learning Models For Moving Vehicle Detection In Traffic Surveillance Video For Smart City Applications
ID_Doc	17912
Authors	Risha K.; Jude Hemanth D.
Year	2023
Published	Communications in Computer and Information Science, 1888 CCIS
DOI	http://dx.doi.org/10.1007/978-3-031-43940-7_1
Abstract	In the last few decades, CCTV surveillance has become a piece of unavoidable equipment in public places. The most important application is for traffic monitoring. Recently, accident cases have been rapidly increasing day by day. So, continuous analysis of vehicles is a challenging task. Developing an algorithm to find a specific vehicle or pedestrian is highly beneficial in such cases. Such a procedure can be done by video processing. Video processing and its applications have improved over the last few years. The advancements in this area are helpful in numerous fields. At the same time, the intervention of artificial intelligence also has a relevant role in this area. Machine learning has also become essential and valuable in various complex cases. Image classification using artificial intelligence provides a highly accurate result rather than any other classification method. This paper mainly compares different convolutional neural networks on moving objects detected by background subtraction and GMM with the morphological operation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Convolutional Neural Network; Smart Cities; Surveillance; Transfer Learning; Video

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