This dataset is about car driver behavior detection. It is a field of research that aims to identify and analyze the actions of drivers while they are driving. In this dataset, we have 5 classes:
- 1 Safe driving
- 2 Talking on the phone
- 3 Texting on the phone
- 4 Turning
- 5 Other activities
Data Source Link: https://www.kaggle.com/code/imtkaggleteam/driver-behavior-detection-cnn
Several types of CNN architectures are used in deep learning-based image analysis. In this project, three of them are being used. AlexNet, VGG16, and ResNet. These are some of the most popular CNN structures used in image analysis tasks.
CNN (Convolutional Neural Network) is a deep learning-based method that can be used for driver behavior detection. CNN is a type of neural network that is designed to recognize patterns in images and videos. CNNs are particularly useful for image classification tasks because they can automatically learn to detect features such as edges, corners, and shapes in images. In driver behavior detection, CNNs are used to analyze video footage of drivers and identify patterns of behavior that may indicate unsafe driving practices.
Driver behavior detection is important because it helps to identify potential cybersecurity threats by tracking user behavior and data access activities. By analyzing both user behavior and data access activities, a behavior analytics tool can create a contextual behavior baseline to distinguish normal from anomalous behaviors and accurately identify critical data threats.