Abstract
Emergency vehicles such as ambulances and fire engines get stuck in a heavy traffic may lead to the loss of valuable lives. To overcome this problem, a framework is proposed to detect emergency vehicle in heavy traffic, using Region-Based Convolutional Neural Network (RCNN) to take quick decisions. When an ambulance comes in a particular direction in a traffic signal, the controller detects and checks for the density of traffic and speed of the vehicle in a particular direction and also calculates the time taken by the emergency vehicle to cross the road. Based on the information received, the controller alerts the alternate roads, by displaying a red signal for a particular time in its path. Once the emergency vehicle passes away from the signal, the master control has to be reset for maintaining the normal traffic flow.
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Uma, K., Sathya Bama, B., Maheesha, M. (2021). Emergency Vehicle Detection in Traffic Surveillance Using Region-Based Convolutional Neural Networks. In: Komanapalli, V.L.N., Sivakumaran, N., Hampannavar, S. (eds) Advances in Automation, Signal Processing, Instrumentation, and Control. i-CASIC 2020. Lecture Notes in Electrical Engineering, vol 700. Springer, Singapore. https://doi.org/10.1007/978-981-15-8221-9_49
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DOI: https://doi.org/10.1007/978-981-15-8221-9_49
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