摘要
针对学生注意力分配困难和对学习影响等问题,提出一种基于机器视觉的精准注意力追踪系统。该系统包括图像采集装置和精准的注意力追踪算法。图像采集装置可以获得更清晰的眼部区域图像。瞳孔中心定位算法用轻量级的MobileNet v3替换VGG16(visual geometry group network),采用两级特征融合和中心关键点预测技术,提高了检测速度和准确率。该算法检测速度可达36帧/s,准确率为97.42%。视线追踪算法旨在解决头部偏移的影响,实现对视线的精确追踪。研发了一款面向学龄儿童的阅读认知评价交互软件。该软件利用采集到的视线坐标计算相关眼动指标,再通过心理学理论分析建模来评估学龄儿童的思维认知能力,为心理学和教育学相关领域研究提供了参考和借鉴。
A precise attention tracking system based on machine vision is designed to address the difficulty in studying students'attention allocation.The system includes an image capture device and an accurate attention tracking algorithm.The image capture device can capture the clearer ocular images.The pupil center localization algorithm replaces VGG16 with lightweight MobileNetv3 and uses twostage feature fusion and center keypoint prediction techniques to improve the speed and accuracy.The algorithm achieves a speed of up to 36 frames/s and 97.42%accuracy.The gaze tracking algorithm compensates for the head movements to achieve the meticulous gaze tracking.An interactive software for assessing cognitive abilities in school-age children is developed.The software calculates the eye movement indicators by using the collected gaze coordinates and evaluates the cognitive abilities based on psychological theory,and provides a reference for the psychology and education research.
作者
刘纪元
祁瀚文
刘志诚
费敏锐
张堃
Liu Jiyuan;Qi Hanwen;Liu Zhicheng;Fei Minrui;Zhang Kun(School of Electrical Engineering,Nantong University,Nantong 226019 China;School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 210053,China;Nantong Key Laboratory of Intelligent Control and Intelligent Computing,Nantong 226019,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2023年第10期2087-2100,共14页
Journal of System Simulation
基金
江苏省产学研合作项目(BY2022224)。
