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Hierarchical modeling for first-person vision activity recognition
مدل سازی سلسله مراتبی برای تشخیص فعالیت بینایی اول شخص-2017 Article history:Received 16 December 2016Revised 15 April 2017Accepted 7 June 2017 Available online xxxCommunicated by Z. WangKeywords:Activity recognition First-person vision Hierarchical modeling Motion featuresTemporal context encodingWe 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 en- code 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-the- art approaches, e.g., with at least 11% improvement in precision and recall on a 15-h public dataset with six ego-centric activities.© 2017 Elsevier B.V. All rights reserved. Keywords: Activity recognition | First-person vision | Hierarchical modeling | Motion features | Temporal context encoding |
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