Zhang Hong, Li Lu, Jia Wenyan, Fernstrom John D, Sclabassi Robert J, Sun Mingui
School of Astronautics, Beihang University, Beijing, CHINA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5569-72. doi: 10.1109/IEMBS.2010.5626794.
A new image based activity recognition method for a person wearing a video camera below the neck is presented in this paper. The wearable device is used to capture video data in front of the wearer. Although the wearer never appears in the video, his or her physical activity is analyzed and recognized using the recorded scene changes resulting from the motion of the wearer. Correspondence features are extracted from adjacent frames and inaccurate matches are removed based on a set of constraints imposed by the camera model. Motion histograms are defined and calculated within a frame and we define a new feature called accumulated motion distribution derived from motion statistics in each frame. A Support Vector Machine (SVM) classifier is trained with this feature and used to classify physical activities in different scenes. Our results show that different types of activities can be recognized in low resolution, field acquired real-world video.
本文提出了一种基于图像的新活动识别方法，用于颈部以下佩戴摄像机的人员。该可穿戴设备用于在佩戴者前方捕获视频数据。虽然佩戴者从未出现在视频中，但通过分析记录的因佩戴者运动而产生的场景变化来识别其身体活动。从相邻帧中提取对应特征，并根据相机模型施加的一组约束去除不准确的匹配。在帧内定义并计算运动直方图，我们还定义了一个名为累积运动分布的新特征，它源自每个帧中的运动统计信息。使用此特征训练支持向量机（SVM）分类器，并用于对不同场景中的身体活动进行分类。我们的结果表明，在低分辨率、实地采集的真实世界视频中可以识别不同类型的活动。
