Abstract
Nowadays, the vehicles in the world are increasing along with the human’s population and one of the issues that causes because of this increasing use of vehicles in traffic. Due to this, it is also getting a hectic task to keep track of vehicles, and one of the best methodologies is by using video recordings of vehicles go by which does not disturb the traffic flow and can be easily installed. There are some techniques for detecting, counting, and tracking the vehicles for traffic flow controlling like point detection which did not reach up to the mark, and our project will present a video-based solution with background subtractor in OpenCV development, and with this, we can detect, count, and track the moving vehicles accurately, and thus, countermeasures can be taken to avoid traffic congestions.
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Prasad, K.S., Pasupathy, S. (2023). A Deep Learning Approach to Analyze Traffic Congestions for Effective Traffic Management. In: Gunjan, V.K., Suganthan, P.N., Haase, J., Kumar, A. (eds) Cybernetics, Cognition and Machine Learning Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1484-3_1
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DOI: https://doi.org/10.1007/978-981-19-1484-3_1
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