• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Video Representation via Fusion of Static and Motion Features Applied to Human Activity Recognition

Vol. 13, No. 7, July 30, 2019
10.3837/tiis.2019.07.015, Download Paper (Free):

Abstract

In human activity recognition system both static and motion information play crucial role for efficient and competitive results. Most of the existing methods are insufficient to extract video features and unable to investigate the level of contribution of both (Static and Motion) components. Our work highlights this problem and proposes Static-Motion fused features descriptor (SMFD), which intelligently leverages both static and motion features in the form of descriptor. First, static features are learned by two-stream 3D convolutional neural network. Second, trajectories are extracted by tracking key points and only those trajectories have been selected which are located in central region of the original video frame in order to to reduce irrelevant background trajectories as well computational complexity. Then, shape and motion descriptors are obtained along with key points by using SIFT flow. Next, cholesky transformation is introduced to fuse static and motion feature vectors to guarantee the equal contribution of all descriptors. Finally, Long Short-Term Memory (LSTM) network is utilized to discover long-term temporal dependencies and final prediction. To confirm the effectiveness of the proposed approach, extensive experiments have been conducted on three well-known datasets i.e. UCF101, HMDB51 and YouTube. Findings shows that the resulting recognition system is on par with state-of-the-art methods.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
S. Arif, J. Wang, Z. Fei and F. Hussain, "Video Representation via Fusion of Static and Motion Features Applied to Human Activity Recognition," KSII Transactions on Internet and Information Systems, vol. 13, no. 7, pp. 3599-3619, 2019. DOI: 10.3837/tiis.2019.07.015.

[ACM Style]
Sheeraz Arif, Jing Wang, Zesong Fei, and Fida Hussain. 2019. Video Representation via Fusion of Static and Motion Features Applied to Human Activity Recognition. KSII Transactions on Internet and Information Systems, 13, 7, (2019), 3599-3619. DOI: 10.3837/tiis.2019.07.015.

[BibTeX Style]
@article{tiis:22159, title="Video Representation via Fusion of Static and Motion Features Applied to Human Activity Recognition", author="Sheeraz Arif and Jing Wang and Zesong Fei and Fida Hussain and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2019.07.015}, volume={13}, number={7}, year="2019", month={July}, pages={3599-3619}}