With the rapid development of the vehicle industry and world economy, the number of private cars continues to increase, which results in more and more traffic accidents, and traffic safety problem has become a global hotpot. The research of driver distracted driving detection is mainly divided into two types:traditional Computer Vision (CV) algorithms and deep learning algorithms. In the driver distraction detection based on traditional CV algorithm, image features are extracted by the feature operators such as Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG), then Support Vector Machine (SVM) is combined to build model and classify the images. However, the traditional CV algorithms have disadvantages of high requirements for the environment, narrow application range, large amount of parameters and high computational complexity. In recent years, deep learning has shown excellent performance such as fast speed and high precision in extracting data features. Therefore, the researchers began to introduce deep learning into driver distracted driving detection. The methods based on deep learning can realize the end-to-end distracted driving detection network with high accuracy. The research status of the traditional CV algorithms and deep learning algorithms in driver distracted driving detection was introduced. Firstly, the situations of the traditional CV algorithms used in the image field and the research of driver distracted driving detection were elaborated. Secondly, the research of driver distracted driving based on deep learning was introduced. Thirdly, the accuracies and model parameters of different driver distracted driving detection methods were compared and analyzed. Finally, the existing research was summarized and three problems that driver distracted driving detection need to solve in the future were put forward:the driver's distraction state and the distraction degree division standards need to be further improved, three aspects of person-car-road need to be considered comprehensively, and how to reduce neural network parameters more effectively.