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Open-view human action recognition based on linear discriminant analysis

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Abstract

In the last decades, action recognition task has evolved from single view recording to unconstrained environment. Recently, multi-view action recognition has become a hot topic in computer vision. However, we notice that only a few works have focused on the open-view action recognition, which is a common problem in the real world. Open-view action recognition focus on doing action recognition in unseen view without using any information from it. To address this issue, we firstly introduce a novel multi-view surveillance action dataset and benchmark several state-of-the-art algorithms. From the results, we observe that the performance of the state-of-the-art algorithms would drop a lot under open-view constraints. Then, we propose a novel open-view action recognition method based on the linear discriminant analysis. This method can learn a common space for action samples under different view by using their category information, which can achieve a good result in open-view action recognition.

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  1. http://mmas.comp.nus.edu.sg/MCAD/MCAD.html

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grant 61772359 and Grant 61472275 and Grant 61572356 and in part by the Tianjin Research Program of Application Foundation and Advanced Technology under Grant 15JCYBJC16200.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Yuting Su, Yang Li & Anan Liu

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  1. Yuting Su
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  2. Yang Li
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  3. Anan Liu
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Correspondence to Anan Liu.

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Su, Y., Li, Y. & Liu, A. Open-view human action recognition based on linear discriminant analysis. Multimed Tools Appl 78, 767–782 (2019). https://doi.org/10.1007/s11042-018-5657-6

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  • DOI: https://doi.org/10.1007/s11042-018-5657-6

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