College of Physics and Information Engineering, Fuzhou University
the National Natural Science Foundation of China under Grant 62071125，the Natural Science Foundation of Fujian Province under Grant 2021J01581and 2018J01805, and the Scientific Research Foundation of Fuzhou University under Grant GXRC-18083
People easily get distracted or tired after long-duration actions such as driving and online classes, which can lead to accidents or poor efficiency. To detect such human behaviors, a head motion detection method based on low-resolution infrared array sensors is proposed with the protection of personal privacy. First, prominent areas of the human body are extracted based on image processing techniques. Then a 3D image fusion algorithm is developed to extract the change information in the spatiotemporal domain. Finally, an improved residual network is developed to achieve head motion classification. Ten head movements are designed for driving and online classroom scenarios. Experimental results show that in the detection range of 50cm to 100cm, our average recognition rate is 96.76%, and the processing speed is 9 frames per second, which is better than the existing state-of-the-art algorithms. The accuracy of the system is 93.7% when it is applied to the vehicle experiment.