This paper presents a study that evaluates the performance of multi-view human activity recognition with videos having degraded quality. For the activity recognition models, a support vector machine-based approach using spatiotemporal features and a deep learning-based approach using convolutional and recurrent layers are built. We investigate the recognition performance of the two models with respect to the bitrate of the compressed videos and the peak signal-to-noise ratio of the videos corrupted by additive Gaussian random noise. We analyze the robustness of the models for the degraded videos.
|Title of host publication||Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2017 Dec 28|
|Event||19th IEEE International Symposium on Multimedia, ISM 2017 - Taichung, Taiwan, Province of China|
Duration: 2017 Dec 11 → 2017 Dec 13
|Name||Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017|
|Other||19th IEEE International Symposium on Multimedia, ISM 2017|
|Country||Taiwan, Province of China|
|Period||17/12/11 → 17/12/13|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT 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) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (NRF-2016R1E1A1A01943283).
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Media Technology
- Sensory Systems