2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
In-Ho Choi , Dept. of Computer Eng., Sejong University, Kwangjin-Gu, Seoul, Korea
Sung Kyung Hong , Dept. of Aerospace, Sejong University, Kwangjin-Gu, Seoul, Korea
Yong-Guk Kim , Dept. of Computer Eng., Sejong University, Kwangjin-Gu, Seoul, Korea
This paper presents a study in which driver's gaze zone is categorized using new deep learning techniques. Since the sequence of gaze zones of a driver reflects precisely what and how he behaves, it allows us infer his drowsiness, focusing or distraction by analyzing the images coming from a camera. A Haar feature based face detector is combined with a correlation filter based MOSS tracker for the face detection task to handle a tough visual environment in the car. Driving database is a big-data which was constructed using a recording setup within a compact sedan by driving around the urban area. The gaze zones consist of 9 categories depending on where a driver is looking at during driving. A convolutional neural network is trained to categorize driver's gaze zone from a given face detected image using a multi-GPU platform, and then its network parameters are transferred to a GPU within a PC running on Windows to operate in the real-time basis. Result suggests that the correct rate of gaze zone categorization reaches to 95% in average, indicating that our system outperforms the state-of-art gaze zone categorization methods based on conventional computer vision techniques.
Face, Vehicles, Databases, Sensors, Cameras, Face detection
In-Ho Choi, Sung Kyung Hong and Yong-Guk Kim, "Real-time categorization of driver's gaze zone using the deep learning techniques," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 143-148.