Traffic Accident Detection

Traffic accident detection research aims to automatically identify accidents in video footage from various sources, improving road safety and traffic management. Current efforts focus on developing robust computer vision models, often employing deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), sometimes combined with optical flow analysis to capture motion information, to overcome challenges posed by diverse accident types, lighting conditions, and occlusions. The availability of large, publicly accessible datasets is crucial for training and evaluating these models, driving progress towards real-time accident detection systems for smart city applications and autonomous vehicles.