International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 15, No. 6, 8 Dec. 2023
Cover page and Table of Contents: PDF (size: 1078KB)
Real Time Accident Detection from Closed Circuit Television and Suggestion of Nearest Medical Amenities
Full Text (PDF, 1078KB), PP.15-28
Views: 0 Downloads: 0
Author(s)
Akanksha A. Pai
1
Harini K. S.
1
Deeptha Giridhar
1
Shanta Rangaswamy
1,*
Index Terms
Accident Detection, Accuracy, Convolutional Neural Network, Traffic Management
Abstract
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
Reference
[1]Abishek, A. T. S., Jose, A., Davis, A., Thomas, A., & Davies, J. (2021). Accident Detection and Warning System. International Journal of Innovative Science and Research Technology, 6(6), 328-332. ISSN: 2456-2165.
[2]Ahmed, B., Imran, M., Zaghdoud, R., Ahmed, M. S., Sendi, R., Alsharif, S., Alabdulkarim, J., Albin Saad, B. A., Alsabt, R., Rahman, A., & Krishnasamy, G. (2023). A real-time computer vision based approach to detection and classification of traffic incidents. Big data and cognitive computing, 7(1), 22. https://doi.org/10.3390/bdcc7010022
[3]Ajith, A., Abhiramy, A., Philip, A. O., Sreedhar, S., & Sreeja, M. U. (2022). Road Accident Detection from CCTV Footages using Deep Learning. 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC),1354-1361.https://doi.org/10.1109/ICOSEC52876.2022.9641484
[4]Al-Mahmoud, A., Alsadeq, A., & Bouktir, T. (2020). A Review of Real-Time Traffic Accident Detection Systems using CCTV. 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA),212-216. https://doi.org/10.1109/ICIEA48418.2020.9373769.
[5]C. K. Gomathy, K. Rohan, B. M. Kiran Reddy, and V. Geetha, "Accident Detection and Alert System," Journal of Engineering, Computing & Architecture, ISSN: 1934-7197.
[6]Deshmukh, S. S., & Wadate, S. A. (2021). Real-Time Accident Detection System Using CCTV Cameras. 2021 International Conference on Electronics, Information, and Communication (ICEIC), 1-5. https://doi.org/10.1109/ICEIC52595.2021.9479301
[7]Gupta, S., & Jain, S. (2019). Accident Detection using Convolutional Networks Neural. 2019 International Conference on Data Science and Communication (IconDSC),1-6. https://doi.org/10.1109/IconDSC47508.2019.9036947
[8]Hooda, R., Kawediya, R., Sonigara, K., & Andhare, S. (2020). Accident Detection System using Surveillance Cameras. International Research Journal of Engineering and Technology (IRJET), 7(4), e-ISSN: 2395-0056.
[9]Ijjina, E. P., Chand, D., Gupta, S., & Goutham, K. (2019). Computer vision-based accident detection in traffic surveillance. 2019 10th International conference on computing, communication and networking technologies (ICCCNT), 1-6. https://doi.org/10.1109/ICCCNT45670.2019.8946176
[10]J. Doe, J. Smith and K. Patel, "Real-Time Accident Detection in Traffic Surveillance Using Deep Learning," in IEEE International Conference on Imaging Systems and Techniques (IST), 2022, pp. 1-6, doi: 10.1109/IST.2022.00001.
[11]Jahagirdar, S., & Koli, S. (2020). Automatic Accident Detection Techniques using CCTV Surveillance Videos: Methods, Data sets and Learning Strategies. International Journal of Engineering and Advanced Technology (IJEAT), 9(3), 2507-2512. https://doi.org/10.35940/ijeat.E4669.029320
[12]Junaid, M., Sultana, S., Qadir, M. A., & Ahmed, F. (2021). Real-time Traffic Accident Detection and Medical Assistance System Based on Computer Vision. IEEE Access, 9, 44813-44827. https://doi.org/10.1109/ACCESS.2021.3079046
[13]M. D. Kulkarni, A. D. Kulkarni, and A. K. Shrivastava, "Accident Detection and Severity Analysis using Deep Learning," presented at the 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICA ECA), Mar. 2021.
[14]Yi, Jichao, Shang, Y., Wang, C., Du, Y., Yang, J., Sun, M., Huang, S., et al. (2023). An intelligent crash recognition method based on 1DResNet-SVM with distributed vibration sensors. Optics Communications, 529, 129263. https://doi.org/10.1016/j.optcom.2021.129263
[15]Zhou, Y., Liu, X., Gao, J., & Li, J. (2020). A Survey of Real-Time Accident Detection and Response Systems. 2020 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), 405-410. https://doi.org/10.1109