14 February 2015 Feature integration with random forests for real-time human activity recognition
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Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 944506 (2015) https://doi.org/10.1117/12.2181201
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy
Abstract
This paper presents an approach for real-time human activity recognition. Three different kinds of features (flow, shape, and a keypoint-based feature) are applied in activity recognition. We use random forests for feature integration and activity classification. A forest is created at each feature that performs as a weak classifier. The international classification of functioning, disability and health (ICF) proposed by WHO is applied in order to set the novel definition in activity recognition. Experiments on human activity recognition using the proposed framework show - 99.2% (Weizmann action dataset), 95.5% (KTH human actions dataset), and 54.6% (UCF50 dataset) recognition accuracy with a real-time processing speed. The feature integration and activity-class definition allow us to accomplish high-accuracy recognition match for the state-of-the-art in real-time.
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Hirokatsu Kataoka, Hirokatsu Kataoka, Kiyoshi Hashimoto, Kiyoshi Hashimoto, Yoshimitsu Aoki, Yoshimitsu Aoki, } "Feature integration with random forests for real-time human activity recognition", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 944506 (14 February 2015); doi: 10.1117/12.2181201; https://doi.org/10.1117/12.2181201
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