Detection method of driver's abnormal behavior modeled based on online behavior

Abstract

The invention relates to a detection method of a driver's abnormal behavior modeled based on an online behavior and belongs to the technical field of image identification and monitoring. The method is based on a video analysis technology, the driver's abnormal behavior is detected by modeling the driver's normal behavior, and the method mainly comprises the following steps of: firstly, initializing and creating an initial model or updating the existing model; secondly, extracting driver's behavior characteristics in a driving process; thirdly, judging whether the driver's behavior is normal according to the initial model and the driver's behavior characteristics; fourthly, updating the model. The detection method is characterized in that the abnormal behavior is detected by a novelty detection way, multiple normal and abnormal behaviors can be treated by adopting a multi-mode modeling method, and false alarms are eliminated by a manual labeling method, thus the steadiness of the solution is increased and the error rate of the solution is reduced.

Description

technical field [0001] The invention relates to a method for detecting abnormal behavior of a driver, in particular to a method for detecting abnormal behavior of a driver based on online behavior modeling. Background technique [0002] Vehicle-mounted vision system has become an emerging application direction in the field of image processing and video analysis. The research on abnormal behavior detection of drivers belongs to the field of intelligent transportation and is a key technology of intelligent assisted driving. This technology detects abnormal behavior of drivers and sends out Warning, to avoid traffic accidents, has important application value and social significance. [0003] At this stage, the research and development in this field in many countries at home and abroad are limited by the characteristics of abnormal behaviors. For example, there are many types of abnormal behaviors, including driver fatigue behaviors such as closing eyes for a long time, yawning,...

AI Technical Summary

Problems solved by technology

Therefore, existing technologies are basically divided into two categories: one is to detect only a specific abnormal behavior, this method only detects fatigue driving behavior, but the specific performance of abnormal behavior of different drivers may be different; the other The reason is that the training method is single. Due to the small number of abnormal behaviors and the difficulty of sample collection, most of the solutions use artificially simulated data for offline training, but the simulated data is usually not real enough, so the trained model is in There is a greater possibility of problems during use, which also makes it difficult to use offline training methods

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