作者
Ye Liu, Liqiang Nie, Li Liu, David S Rosenblum
发表日期
2016/3/12
期刊
Neurocomputing
卷号
181
页码范围
108-115
出版商
Elsevier
简介
As compared to actions, activities are much more complex, but semantically they are more representative of a human׳s real life. Techniques for action recognition from sensor-generated data are mature. However, few efforts have targeted sensor-based activity recognition. In this paper, we present an efficient algorithm to identify temporal patterns among actions and utilize the identified patterns to represent activities for automated recognition. Experiments on a real-world dataset demonstrated that our approach is able to recognize activities with high accuracy from temporal patterns, and that temporal patterns can be used effectively as a mid-level feature for activity representation.
引用总数
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