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Simultaneous context and motion learning in video prediction

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Abstract

Video prediction aims to generate future frames from the past several given frames. It has many applications for abnormal action recognition, future traffic prediction, long-term planning and autonomous driving. Recently, various deep learning-based methods have been proposed to address this task. However, these methods seem only to focus on increasing the network performance and ignore the computational cost problem of them. Even, several methods require two separate networks to perform with two different input types such as RGB, temporal gradient and optical flow. This makes them more and more complex and requires a extremely huge computational cost and memory space. In this paper, we introduce a simple yet robust approach to learn simultaneous both appearance and motion features in only a network regardless diversity of input video modalities. Moreover, we also present a lightweight autoencoder network for addressing this issue. Our framework is conducted on various benchmarks such as KTH, KITTI and BAIR datasets. The experimental results have shown that our approach achieves competitive performance compared to state-of-the-art video prediction methods with only 34.24MB of memory space and 2.59GFLOPs. With a smaller model size and less computational cost, our framework can run faster with a small inference time compared to the other methods. Besides, it only with 2.934 s to predict the next frame, our framework is a promising approach to deploy on embedded or mobile devices without GPU in real time.

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Data availability

All datasets used in this work are public available in the Internet.

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Funding

This work is supported in part by the Thai Nguyen University of Education under Grant TNUE-2022-03.

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Authors and Affiliations

  1. Department of CSIS, Thai Nguyen University of Education, Thai Nguyen, Vietnam

    Duc-Quang Vu

  2. Department of CSIE, National Central University, Taoyuan, Taiwan

    Duc-Quang Vu

  3. Thai Nguyen University, Thai Nguyen, Vietnam

    Trang Phung T. Thu

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  1. Duc-Quang Vu
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  2. Trang Phung T. Thu
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Contributions

(1) Quang proposed this idea. (2) All authors proceed to code, experiment and compare the results of the proposed approach with state-of-the-art methods. (3) All authors wrote the draft of the manuscript and prepared tables. (4) Quang prepared all figures. (5) All authors revised and proofread the manuscript. (6) All authors reviewed the manuscript. (7) Trang submitted the manuscript.

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Correspondence to Trang Phung T. Thu.

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Vu, DQ., Thu, T.P.T. Simultaneous context and motion learning in video prediction. SIViP 17, 3933–3942 (2023). https://doi.org/10.1007/s11760-023-02623-x

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