Multimedia Surveillance and Monitoring

2006 ◽  
Author(s):  
Mohan Kankanhalli
Keyword(s):  
Multimedia Surveillance

TripRes

2021 ◽  
Vol 17 (2) ◽  
pp. 1-21
Author(s):  
Xiaolong Xu ◽  
Zijie Fang ◽  
Lianyong Qi ◽  
Xuyun Zhang ◽  
Qiang He ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Discrete Distribution ◽  
Resource Provisioning ◽  
Path Selection ◽  
Urban Traffic ◽  
Traffic Flows ◽  
Multimedia Services ◽  
Traffic Flow Prediction ◽  
The City ◽  
Multimedia Surveillance

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.

scholarly journals A robust multimedia surveillance system for people counting

2016 ◽  
Vol 76 (22) ◽  
pp. 23777-23804 ◽  
Author(s):  
Zeyad Q. H. Al-Zaydi ◽  
David L. Ndzi ◽  
Munirah L. Kamarudin ◽  
Ammar Zakaria ◽  
Ali Y. M. Shakaff
Keyword(s):  
Surveillance System ◽  
People Counting ◽  
Multimedia Surveillance

scholarly journals Information assimilation framework for event detection in multimedia surveillance systems

2006 ◽  
Vol 12 (3) ◽  
pp. 239-253 ◽  
Author(s):  
Pradeep Kumar Atrey ◽  
Mohan S. Kankanhalli ◽  
Ramesh Jain
Keyword(s):  
Event Detection ◽  
Surveillance Systems ◽  
Information Assimilation ◽  
Multimedia Surveillance

Distributed architectures and logical-task decomposition in multimedia surveillance systems

2001 ◽  
Vol 89 (10) ◽  
pp. 1419-1440 ◽  
Author(s):  
L. Marcenaro ◽  
F. Oberti ◽  
G.L. Foresti ◽  
C.S. Regazzoni
Keyword(s):  
Surveillance Systems ◽  
Distributed Architectures ◽  
Task Decomposition ◽  
Multimedia Surveillance
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