Accident Detection Using Convolutional Neural Networks
- Title
- Accident Detection Using Convolutional Neural Networks
- Creator
- Ghosh S.; Sunny S.J.; Roney R.
- Description
- Accidents have been a major cause of deaths in India. More than 80% of accident-related deaths occur not due to the accident itself but the lack of timely help reaching the accident victims. In highways where the traffic is really light and fast-paced an accident victim could be left unattended for a long time. The intent is to create a system which would detect an accident based on the live feed of video from a CCTV camera installed on a highway. The idea is to take each frame of a video and run it through a deep learning convolution neural network model which has been trained to classify frames of a video into accident or non-accident. Convolutional Neural Networks has proven to be a fast and accurate approach to classify images. CNN based image classifiers have given accuracy's of more than 95% for comparatively smaller datasets and require less preprocessing as compared to other image classifying algorithms. 2019 IEEE.
- Source
- 2019 International Conference on Data Science and Communication, IconDSC 2019
- Date
- 2019-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Accident Detection; Convolutional Neural Network; Deep Learning; Recurrent Neural Network; Video Classification
- Coverage
- Ghosh S., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Sunny S.J., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Roney R., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-153869319-3
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Ghosh S.; Sunny S.J.; Roney R., “Accident Detection Using Convolutional Neural Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20780.