Temporal Modeling on Multi-Temporal-Scale Spatiotemporal Atoms for Action Recognition
As an important branch of video analysis, human action recognition has attracted extensive research attention in computer vision and artificial intelligence communities. In this paper, we propose to model the temporal evolution of multi-temporal-scale atoms for action recognition. An action can be c...
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| Vydané v: | Applied sciences Ročník 8; číslo 10; s. 1835 |
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| Abstract | As an important branch of video analysis, human action recognition has attracted extensive research attention in computer vision and artificial intelligence communities. In this paper, we propose to model the temporal evolution of multi-temporal-scale atoms for action recognition. An action can be considered as a temporal sequence of action units. These action units which we referred to as action atoms, can capture the key semantic and characteristic spatiotemporal features of actions in different temporal scales. We first investigate Res3D, a powerful 3D CNN architecture and create the variants of Res3D for different temporal scale. In each temporal scale, we design some practices to transfer the knowledge learned from RGB to optical flow (OF) and build RGB and OF streams to extract deep spatiotemporal information using Res3D. Then we propose an unsupervised method to mine action atoms in the deep spatiotemporal space. Finally, we use long short-term memory (LSTM) to model the temporal evolution of atoms for action recognition. The experimental results show that our proposed multi-temporal-scale spatiotemporal atoms modeling method achieves recognition performance comparable to that of state-of-the-art methods on two challenging action recognition datasets: UCF101 and HMDB51. |
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| AbstractList | As an important branch of video analysis, human action recognition has attracted extensive research attention in computer vision and artificial intelligence communities. In this paper, we propose to model the temporal evolution of multi-temporal-scale atoms for action recognition. An action can be considered as a temporal sequence of action units. These action units which we referred to as action atoms, can capture the key semantic and characteristic spatiotemporal features of actions in different temporal scales. We first investigate Res3D, a powerful 3D CNN architecture and create the variants of Res3D for different temporal scale. In each temporal scale, we design some practices to transfer the knowledge learned from RGB to optical flow (OF) and build RGB and OF streams to extract deep spatiotemporal information using Res3D. Then we propose an unsupervised method to mine action atoms in the deep spatiotemporal space. Finally, we use long short-term memory (LSTM) to model the temporal evolution of atoms for action recognition. The experimental results show that our proposed multi-temporal-scale spatiotemporal atoms modeling method achieves recognition performance comparable to that of state-of-the-art methods on two challenging action recognition datasets: UCF101 and HMDB51. |
| Author | Lei, Tao Liu, Xianyuan Jiang, Ping Yao, Guangle |
| Author_xml | – sequence: 1 givenname: Guangle orcidid: 0000-0002-3030-1598 surname: Yao fullname: Yao, Guangle – sequence: 2 givenname: Tao orcidid: 0000-0002-0900-1582 surname: Lei fullname: Lei, Tao – sequence: 3 givenname: Xianyuan orcidid: 0000-0002-3084-519X surname: Liu fullname: Liu, Xianyuan – sequence: 4 givenname: Ping surname: Jiang fullname: Jiang, Ping |
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| SubjectTerms | action atom action recognition Classification convolutional neural network International conferences long short-term memory Methods Neural networks Pattern recognition Semantics |
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