Traffic Collision Detection Using DenseNet
Traffic Collision Detection Using DenseNet
- New search for: Kaluza, Daniel
- New search for: Seiler, Marco
- New search for: Kashef, Rasha
- New search for: Kaluza, Daniel
- New search for: Seiler, Marco
- New search for: Kashef, Rasha
There has been a sharp rise in the number of fatalities and injuries caused by traffic accidents in urban areas. Cities often have video and image resources that can be analyzed manually using operators to address this problem. This paper introduces an automated collision detection system that utilizes publicly available images captured by Toronto's traffic camera system. The system is based on a Deep Learning model, specifically a DenseNet-161, employed to classify accidents and non-accidents. The results of this classification are then displayed on a graphical user interface. The primary aim of this study is to reduce medical response time and ultimately save lives by issuing automatic alerts. The proposed system has the potential to minimize the severity of accidents and decrease the number of fatalities by notifying emergency services once an accident is detected.
Document information
Traffic Collision Detection Using DenseNet
2023-12-18
1305677 byte
Conference paper
Electronic Resource
English
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