期刊
NEUROCOMPUTING
卷 267, 期 -, 页码 362-377出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2017.06.015
关键词
Activity recognition; First-person vision; Hierarchical modeling; Motion features; Temporal context encoding
资金
- Erasmus Mundus Joint Doctorate in Interactive and Cognitive Environments - EACEA Agency of the European Commission under EMJD ICE FPA [2010-2012]
We propose a multi-layer framework to recognize ego-centric activities from a wearable camera. We model the activities of interest as hierarchy based on low-level feature groups. These feature groups encode motion magnitude, direction and variation of intra-frame appearance descriptors. Then we exploit the temporal relationships among activities to extract a high-level feature that accumulates and weights past information. Finally, we define a confidence score to temporally smooth the classification decision. The results across multiple public datasets show that the proposed framework outperforms state-of-theart approaches, e.g., with at least 11% improvement in precision and recall on a 15-h public dataset with six ego-centric activities. (C) 2017 Elsevier B.V. All rights reserved.
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