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Real-Time Action Recognition with Deeply-Transferred Motion Vector CNNs.

Bowen Zhang, Limin Wang, Zhe Wang

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    This study introduces a faster real-time action recognition method using motion vectors instead of optical flow. Four novel training strategies significantly improve accuracy, achieving over 390 FPS.

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Two-stream Convolutional Neural Networks (CNNs) are effective for video action recognition.
    • Calculating optical flow for these networks is computationally expensive and time-consuming.

    Purpose of the Study:

    • To develop a real-time action recognition system that overcomes the time cost of optical flow calculation.
    • To enhance the accuracy of CNNs trained with motion vectors by leveraging knowledge from optical flow models.

    Main Methods:

    • Replaced optical flow with motion vectors, which are directly extractable from video streams.
    • Developed four knowledge transfer training strategies to improve motion vector-based CNN accuracy.
    • Proposed a deeply transferred motion vector CNN architecture.

    Main Results:

    • The proposed method achieves a processing speed of 390.7 frames per second, exceeding real-time requirements.
    • Experimental results on various datasets demonstrate the effectiveness of the proposed training strategies.
    • The motion vector-based approach significantly outperforms traditional optical flow methods in speed.

    Conclusions:

    • Motion vectors can effectively replace optical flow for real-time action recognition.
    • Knowledge transfer from optical flow CNNs is crucial for enhancing motion vector CNN accuracy.
    • The developed approach offers a computationally efficient and accurate solution for video action recognition.