单摄像机下基于眼动分析的行为识别

眼动信息是识别观看视频、浏览网页等以视觉任务为主的行为的关键信息. 针对传统的可穿戴传感器普遍具有侵入性, 而现有基于视觉的眼动仪存在价格昂贵、校准过程复杂等问题, 本文尝试使用单一的标准网络摄像头获取眼动信息用于行为识别, 并评估了该方法的可行性. 提出一种针对低质量视频图像的虹膜跟踪算法以获取眼动信号, 然后分别从水平和垂直方向的眼动信号中提取出五种新的眼动特征, 并从中选择出最优特征子集, 最后采用支持向量机分类器评价了本文方法的可行性. 针对不同应用背景设计了三组验证实验: 留一交叉验证、k折交叉验证及单独校准测试, 三组实验中, 对不同参与者三种行为的平均识别正确率分别为68.4%, 79.3%及84.1%, 证明了基于视频图像的眼动分析用于行为识别是一种很有希望的传感形式, 并有望用于更为复杂的传感任务. 关键词： 物联网 行为识别 眼睛跟踪 传感

Eye movement analysis for activity recognition based on one web camera

Eye movement information is the key clue for recognizing the vision-dominated tasks, such as browsing the web, or watching a video. However, traditional wearable sensors are invasive and the vision-based eye trackers are very expensive and need time consuming calibration. Therefore, an activity recognition method based on eye movement analysis under one web camera is first proposed and the feasibility is assessed. First, an iris tracking method for the low quality image is proposed to acquire eye movement information. Then, five ten novel features are extracted from the horizontal and the vertical eye movement signals for activity recognition, and the optimal feature subset is selected. Finally, the support vector machine is used to assess the feasibility of the proposed method. Three experiments are designed for different applications: leave-one-out cross-validation, k-fold cross-validation, and validation after respective calibration. Experimental results show that their accuracies are 68.4%, 79.3% and 84.1%, respectively, which demonstrate the promise of eye based activity recognition using one web camera.
Keywords： internet of things activity recognition eye tracking sensing