Human activity recognition is one of the important research topics in computer vision and video understanding. It is often assumed that high quality video sequences are available for recognition. However, relaxing such a requirement and implementing robust recognition using videos having reduced data rates can achieve efficiency in storing and transmitting video data. Three-dimensional video scalability, which refers to the possibility of reducing spatial, temporal, and quality resolutions of videos, is an effective way for flexible representation and management of video data. In this paper, we investigate the impact of the video scalability on multiview activity recognition.We employ both a spatiotemporal feature extraction-based method and a deep learning-based method using convolutional and recurrent neural networks. The recognition performance of the two methods is examined, along with in-depth analysis regarding how their performance vary with respect to various scalability combinations. In particular, we demonstrate that the deep learning-based method can achieve significantly improved robustness in comparison to the feature-based method. Furthermore, we investigate optimal scalability combinations with respect to bitrate in order to provide useful guidelines for an optimal operation policy in resource-constrained activity recognition systems.
|Title of host publication||Thematic Workshops 2017 - Proceedings of the Thematic Workshops of ACM Multimedia 2017, co-located with MM 2017|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||9|
|Publication status||Published - 2017 Oct 23|
|Event||1st International ACM Thematic Workshops, Thematic Workshops 2017 - Mountain View, United States|
Duration: 2017 Oct 23 → 2017 Oct 27
|Name||Thematic Workshops 2017 - Proceedings of the Thematic Workshops of ACM Multimedia 2017, co-located with MM 2017|
|Other||1st International ACM Thematic Workshops, Thematic Workshops 2017|
|Period||17/10/23 → 17/10/27|
Bibliographical noteFunding Information:
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding).
© 2017 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computational Theory and Mathematics
- Theoretical Computer Science