This paper introduces a new surveillance platform which is equipped with multiple parallel deep learning frameworks. The deep learning frameworks are used for the face recognition of input image and video streams from CCTV cameras in security applications. Each deep learning framework has its own accuracy (related to recognition performance) and operation time (related to system stability) those are in tradeoff relationship. Based on this system architecture, a new dynamic control algorithm which selects one deep learning framework for time-average security-level (i.e., machine learning accuracy for recognition and classification) maximization under the consideration of system stability. The performance of the proposed algorithm was evaluated and also verified that it achieves desired performance.
|Title of host publication||Proceedings - 2017 IEEE Symposium on Privacy-Aware Computing, PAC 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||2|
|State||Published - 4 Dec 2017|
|Event||1st IEEE Symposium on Privacy-Aware Computing, PAC 2017 - Washington, United States|
Duration: 1 Aug 2017 → 3 Aug 2017
|Name||Proceedings - 2017 IEEE Symposium on Privacy-Aware Computing, PAC 2017|
|Conference||1st IEEE Symposium on Privacy-Aware Computing, PAC 2017|
|Period||1/08/17 → 3/08/17|
Bibliographical noteFunding Information:
ACKNOWLEDGEMENT This work was supported by National Research Foundation of Korea (No: 2016R1C1B1015406).
© 2017 IEEE.
- Deep Learning
- Lyapunov Optimization