Real Time Accident Detection from Closed Circuit Television and Suggestion of Nearest Medical Amenities

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Akanksha A. Pai 1 Harini K. S. 1 Deeptha Giridhar 1 Shanta Rangaswamy 1,*

1. Department of Computer Science and Engineering, RV College of Engineering, Bengaluru, India

* Corresponding author.


Received: 30 Jun. 2023 / Revised: 18 Aug. 2023 / Accepted: 21 Oct. 2023 / Published: 8 Dec. 2023

Index Terms

Accident Detection, Accuracy, Convolutional Neural Network, Traffic Management


The prevalence of automobile accidents as a major cause of violent deaths around the world has prompted researchers to develop an automated method for detecting them. The effectiveness of medical response to accident scenes and the chances of survival are influenced by the human element, underscoring the need for an automated system. With the widespread use of video surveillance and advanced traffic systems, researchers have proposed a model to automatically detect traffic accidents on video. The proposed approach assumes that visual elements occurring in a temporal sequence correspond to traffic accidents. The model architecture consists of two phases: visual feature extraction and temporal pattern detection. Convolution and recurrent layers are employed during training to learn visual and temporal features from scratch as well as from publicly available datasets. The proposed accident detection and alerting system using Convolution Neural Network models with Rectified Linear Unit and Softmax activation functions is an effective tool for detecting different types of accidents in real-time. The system of accident detection, integrated with the alerting mechanism for prompt medical assistance achieved high accuracy and recall rates.

Cite This Paper

Akanksha A. Pai, Harini K. S., Deeptha Giridhar, Shanta Rangaswamy, "Real Time Accident Detection from Closed Circuit Television and Suggestion of Nearest Medical Amenities", International Journal of Information Technology and Computer Science(IJITCS), Vol.15, No.6, pp.15-28, 2023. DOI:10.5815/ijitcs.2023.06.02


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