Taxi Drivers' Smoking Behavior Detection in Traffic Monitoring Video
Si-Wei Chen, Kebin Jia, Pengyu Liu, Xunping Huang · 2019
Taxi is an indispensable part of the urban public transportation industry. However, the existence of illegal operating behaviors brings security risks to passengers and affects the city's civilization. The paper studies the detection methods of taxi drivers' smoking behavior during operation in the surveillance video environment. Firstly, the Vibe algorithm is used to detect the motion foreground of the video. Then the Haar-Adaboost algorithm is used to identify the taxi and extract the target detection area. The HSV color model is used to extract the color characteristics of the smoking smoke, and separate the target area from the moving objects which is similar to smoking smoke. Finally, combined changes in shape and movement characteristics of smoking smoke to further eliminate the interference objects, and the final detection results are obtained. The experimental results show that this algorithm has high realtime and accuracy, which is beneficial to off-site law enforcement in intelligent traffic management.