|Thesis Advisor||熊刚 ; 苟超|
|Place of Conferral||中国科学院自动化研究所|
|Keyword||复杂驾驶场景 级联联合回归 堆叠沙漏网络 人眼检测 视线协同估计|
In order to reduce traffic accidents, the research on driving monitoring technology has attracted wide attention. Eye detection and gaze estimation are important parts of driving monitoring research. It is of great significance to study eye detection and gaze estimation method in complex driving scenes.At present, there are many methods and results of eye detection and gaze estimation in a specific environment or when the environment is controllable. However, for complex driving scenes, those factors like illumination, distance, background, angle and expression increase the difficulty of eye detection and gaze estimation. Moreover, there are still some problems in the current research. For example, existing models do not consider the relationship between eye-related key points and gaze, and pay too much attention to accuracy of model and neglect the number of parameters, which restrict practical application and promotion of eye detection and gaze estimation.
In this dissertation, the driver's eyes in complex driving scenes are taken as research object. The methods of eye detection and gaze estimation are studied and analyzed. To address aforementioned problems and challenges, the corresponding solutions are put forward. The main research contents are as follows:
1. In this dissertation, eye region detection based on face landmarks in complex driving scenes is studied. For the low accuracy of directly detect eye region in image, this dissertation is on the basis of existing algorithm for face detection, then face landmarks are marked based on face region, and then the eye region is extracted according to the relative position of face landmarks. This method is simple, effective and easy to implement. It can detect eye region in the scenes of complex light and partial occlusion.
2. In this dissertation, cooperative eye detection and gaze estimation based on cascaded joint regression model is proposed. Aiming at the problem of modeling the relationship between eye-related key points and gaze, cascaded joint regression algorithm is proposed in this dissertation. It uses multi-feature fusion strategy to realize the cooperative relationship modeling, and realizes eye detection and gaze estimation simultaneously. The results on test datasets show that when detection error is within the pupil radius, accuracy of eye detection can be improved by about 3% and gaze estimation error can be reduced by about 1 degree. The experimental results validate effectiveness of proposed cascade joint algorithm, and show great potential of cascade joint regression algorithm in the application of eye detection and gaze estimation in complex driving scenes.
3. In this dissertation, cooperative eye detection and gaze estimation based on deep stacked hourglass network model is studied. In order to ensure accuracy and reduce the number of parameters, deep stacked hourglass network is proposed. According to the difference of tasks among sub-networks, different scale sub-modules are stacked. At the same time, eye model is established to estimate gaze, so as to realize eye detection and gaze estimation. A large number of images generated by improved SimGAN are used to train deep stacked hourglass network. The experimental results on test datasets show that accuracy of eye detection and gaze estimation error can reach 99.3% and 9.5 degrees respectively, and the number of parameters can be reduced.
In summary, this dissertation focuses on eye region detection, linear cascade joint regression and non-linear deep stacked hourglass network model to achieve cooperative eye detection and gaze estimation. The proposed method can improve the accuracy of eye detection and gaze estimation. It is of great significance to research on monitoring technology in actual driving scenes.
|曹琳. 复杂驾驶场景下协同式的人眼检测及视线估计方法研究[D]. 中国科学院自动化研究所. 中国科学院大学,2019.|
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