An Investigation Approach Used for Pattern Classification and Recognition of an Emergency Vehicle

Gowtham, P.; Eswari, P.; Arunachalam, Vasan

doi:10.1109/icsns.2018.8573610

Cited by 2 publications

(1 citation statement)

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“…Later in [20], neural network (NN) and structural traits are used to train and recognize ambulance characters in emergency vehicles. The authors explore the photography processing machine through "Thinning" and "Hilditch" algorithms, and the structural features of a character in the picture.…”

Section: Emergency Vehicle Classificationmentioning

confidence: 99%

Deep convolutional neural networks architecture for an efficient emergency vehicle classification in real-time traffic monitoring

Kherraki

Ouazzani

2022

IJ-AI

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Nowadays, intelligent transportation system (ITS) has become one of the most popular subjects of scientific research. ITS provides innovative services to traffic monitoring. The classification of emergency vehicles in traffic surveillance cameras provides an early warning to ensure a rapid reaction in emergency events. Computer vision technology, including deep learning, has many advantages for traffic monitoring. For instance, convolutional neural network (CNN) has given very good results and optimal performance in computer vision tasks, such as the classification of vehicles according to their types, and brands. In this paper, we will classify emergency vehicles from the output of a closed-circuit television (CCTV) camera. Among the advantages of this research paper is providing detailed information on the emergency vehicle classification topic. Emergency vehicles have the highest priority on the road and finding the best emergency vehicle classification model in realtime will undoubtedly save lives. Thus, we have used eight CNN architectures and compared their performances on the Analytics Vidhya Emergency Vehicle dataset. The experiments show that the utilization of DenseNet121 gives excellent classification results which makes it the most suitable architecture for this research topic, besides, DenseNet121 does not require a high memory size which makes it appropriate for real-time applications.<p> </p>

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Section: Emergency Vehicle Classificationmentioning

confidence: 99%