Accident Detection using Images and Videos with CNN, LSTM, and Interpreting the Results using LIME & GradCAM

Accident detection is a crucial issue in modern society, as accidents can have devastating consequences for those involved. One way to improve accident detection is through the use of images and videos, which can provide valuable information about the scene of an accident. In this paper, we explore the use of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and explainable AI concepts for detecting accidents using images and videos. Each frame of a video is processed through a deep learning convolution neural network model that has been trained to identify frames of a video as accident or non-accident. We also incorporate RNNs into our model, as they are ideal for processing sequential data such as video frames. In addition to using CNNs and RNNs, we also incorporate explainable AI concepts into our model. This allows us to provide explanations for our model's predictions, which can help build trust in the system and improve its overall effectiveness. Our initial results show that our approach is effective at detecting accidents using images and videos. With impressive accuracy, our model can categorize frames of a movie as accident or non-accident. Additionally, using explainable AI concepts allows us to provide clear explanations for our model's predictions.