TY - GEN
T1 - Edge-centric Video Surveillance System Based on Event-driven Rate Adaptation for 24-hour Monitoring
AU - Sakaushi, Airi
AU - Kanai, Kenji
AU - Katto, Jiro
AU - Tsuda, Toshitaka
N1 - Funding Information:
ACKNOWLEDGMENT This research is supported by “Research and Development on Fundamental and Utilization Technologies for Social Big Data”, NICT, Japan and Grant-in-Aid for Scientific Research (A) (15H01684) of JSPS, Japan.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/2
Y1 - 2018/10/2
N2 - In this paper, to sustain a high-quality 24-hour video surveillance (i.e., high reliability) and reduce redundant video traffic volume (i.e., network friendliness), we propose an edge-centric video surveillance system that provides flexible adaptive control of the image enhancement process and video quality based on an event-driven adaptation. In the proposed system, the video bitrate is adaptively controlled according to the contrast of captured videos and conditions in a monitored area (e.g., 'normal', 'caution', and 'alert'). To confirm the system performance, we evaluate objective image quality, accuracy of human detection and video traffic volume generated by the proposed system. Evaluations conclude that the system can reduce the video traffic while sustaining high-quality visibility.
AB - In this paper, to sustain a high-quality 24-hour video surveillance (i.e., high reliability) and reduce redundant video traffic volume (i.e., network friendliness), we propose an edge-centric video surveillance system that provides flexible adaptive control of the image enhancement process and video quality based on an event-driven adaptation. In the proposed system, the video bitrate is adaptively controlled according to the contrast of captured videos and conditions in a monitored area (e.g., 'normal', 'caution', and 'alert'). To confirm the system performance, we evaluate objective image quality, accuracy of human detection and video traffic volume generated by the proposed system. Evaluations conclude that the system can reduce the video traffic while sustaining high-quality visibility.
KW - Edge computing
KW - Image enhancement
KW - IoT
KW - Rate adaptation
KW - Video surveillance system
UR - http://www.scopus.com/inward/record.url?scp=85056467828&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056467828&partnerID=8YFLogxK
U2 - 10.1109/PERCOMW.2018.8480272
DO - 10.1109/PERCOMW.2018.8480272
M3 - Conference contribution
AN - SCOPUS:85056467828
T3 - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
SP - 651
EP - 656
BT - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
Y2 - 19 March 2018 through 23 March 2018
ER -