Abstract:
Human action recognition which recognizes human actions in a video is a fundamental task in computer vision field. Although multiple existing methods with single-view or ...Show MoreMetadata
Abstract:
Human action recognition which recognizes human actions in a video is a fundamental task in computer vision field. Although multiple existing methods with single-view or multi-view have been presented for human action recognition, these recognition approaches cannot be extended into new action recognition or action classification tasks, as well as discover underlying correlations among different views. To tackle the above problem, this paper proposes a new lifelong multi-view subspace learning framework for continuous human action recognition, which could exploit the complementary information amongst different views from a lifelong learning perspective. More specifically, a set of view-specific libraries is established to gradually store the useful information within multiple views. As a new action recognition task comes, we decompose the model parameters into a set of embedded parameters over view-specific libraries. A latent representation subspace is constructed via encouraging it to be close to different view-specific libraries, which can leverage the high-order correlations among different views and further avoid partial information for action recognition task. Meanwhile, we propose to employ an alternating direction strategy to optimize our proposed method. Empirical studies on real-world multi-view action recognition datasets have shown that our proposed framework attains the superior recognition performance and saves the computational time when continually learning new action recognition tasks.
Published in: IEEE Transactions on Circuits and Systems for Video Technology ( Volume: 32, Issue: 6, June 2022)
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- IEEE Keywords
- Index Terms
- Human Activities ,
- Continuous Action ,
- Action Recognition ,
- Human Activity Recognition ,
- Continuous Recognition ,
- Learning Framework ,
- Recognition Task ,
- Lifelong Learning ,
- Recognition Performance ,
- Incremental Learning ,
- Latent Representation ,
- Recognition Framework ,
- Set Of Libraries ,
- Multi-view Learning ,
- Action Recognition Task ,
- Action Recognition Datasets ,
- Learning Models ,
- Convolutional Neural Network ,
- Social Cognition ,
- Learning Task ,
- Multi-task Learning ,
- Nonlinear Correlation ,
- Multi-task Learning Model ,
- Kernel Matching ,
- Catastrophic Forgetting ,
- Efficient Learning ,
- Gram Matrix ,
- Sequential Task ,
- Half Of The Sample ,
- Bag Of Visual Words
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Human Activities ,
- Continuous Action ,
- Action Recognition ,
- Human Activity Recognition ,
- Continuous Recognition ,
- Learning Framework ,
- Recognition Task ,
- Lifelong Learning ,
- Recognition Performance ,
- Incremental Learning ,
- Latent Representation ,
- Recognition Framework ,
- Set Of Libraries ,
- Multi-view Learning ,
- Action Recognition Task ,
- Action Recognition Datasets ,
- Learning Models ,
- Convolutional Neural Network ,
- Social Cognition ,
- Learning Task ,
- Multi-task Learning ,
- Nonlinear Correlation ,
- Multi-task Learning Model ,
- Kernel Matching ,
- Catastrophic Forgetting ,
- Efficient Learning ,
- Gram Matrix ,
- Sequential Task ,
- Half Of The Sample ,
- Bag Of Visual Words
- Author Keywords