Abstract
Representing the content of the video by a motion map is a challenging problem in video analysis. This paper proposes to integrate the discriminative information of a video into a map by optimizing the recognition accuracy of the original video in the action recognition task. The motion map represents a prefix of video frames sequence. A motion map and the next video frame can be integrated to a new motion map by the proposed 3-dimensional convolution based model. This model can be trained by incremental length clips from training videos iteratively, and the final acquired network can be used for generating the motion map of the whole video. Experimental results on the UCF101 and the HMDB51 datasets show that our method achieves better results compared with other related methods.
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Acknowledgments
This work was supported in part by the Natural Science Foundation of China (NSFC) under Grants No. 61673062, No. 61472038 and No. 61375044.
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Yu, W., Sun, Y., Yu, F., Wu, X. (2018). Representing Discrimination of Video by a Motion Map. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_67
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DOI: https://doi.org/10.1007/978-3-319-77380-3_67
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