An Optimization Model for Human Activity Recognition Inspired by Information on Human-Object Interaction
| Autor: | Xinhua Liu, Xiaolin Ma, Tianyu You, Hailan Kuang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Feature extraction 02 engineering and technology Solid modeling 010501 environmental sciences Object (computer science) 01 natural sciences Motion (physics) Object detection Activity recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Guitar business 0105 earth and related environmental sciences |
| Zdroj: | 2018 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). |
| Popis: | Human activity categories can be mainly divided into two types: body motion (e.g. "dancing" and "jumping") and human-object interaction (e.g. "playing guitar" and "riding bike"). In this paper, we propose a model that uses this insight to combine spatiotemporal features with information on human-object interaction to predict human activity categories in realistic videos. In this paper, we (1) improve on prior work by proposing a model to provide long-term prediction based on spatiotemporal features extracted by a deep 3-dimensional convolutional network and (2) extend the spatiotemporal features by combining with information on human-object interaction generated from an object detection model. Our model achieves 92.5% accuracy on UCF101 dataset that outperforms any other state-of-the-art methods. In addition, our approach has high computing efficiency and achieves real-time processing. |
| Databáze: | OpenAIRE |
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