Video traffic analytics for large scale surveillance

被引:0
作者
Soumen Kanrar
Niranjan Kumar Mandal
机构
[1] Vehere Interactive Pvt Ltd,Department of Computer Science
[2] Vidyasagar University,undefined
来源
Multimedia Tools and Applications | 2017年 / 76卷
关键词
Interactive session; Video on demand; Surveillance; Mixed strategy; Topology; Compact space; Mesh structure; Hybrid architecture;
D O I
暂无
中图分类号
学科分类号
摘要
The video traffic analysis is the most important issue for large scale surveillance. In the large scale surveillance system, huge amount of live digital video data is submitted to the storage servers through the number of externally connected scalable components. The system also contains huge amount of popular and unpopular old videos in the archived storage servers. The video data is delivered to the viewers, partly or completely on demand through a compact system. In real time, huge amount of video data is imported to the viewer’s node for various analysis purposes. The viewers use a number of interactive operations during the real time tracking suspect. The compact video on demand system is used in peer to peer mesh type hybrid architecture. The chunk of video objects move fast through the real time generated compact topological space. Video traffic analytics is required to transfer compressed multimedia data efficiently. In this work, we present a dynamically developed topological space, using mixed strategy by game approach to move the video traffic faster. The simulation results are well addressed in real life scenario.
引用
收藏
页码:13315 / 13342
页数:27
相关论文
共 36 条
[21]  
Shafique K(2007)Push-to-peer video-on-demand system: design and evaluation IEEE J Sel Areas Commun 25 1706-579
[22]  
Shah M(2013)Optimal content placement for peer-to-peer video-on-demand systems. Networking IEEE/ACM Trans Networking 21 566-1109
[23]  
Saroj Kumar R(1996)Performance model of interactive video-on-demand systerns IEEE J Sel Areas Commun 14 1099-848
[24]  
Shah M(2012)Distributed and Online Fair Resource Managment in Video Surveillance Sensor Networks IEEE Trans Mob Comput 11 835-undefined
[25]  
Shiang H-P(undefined)undefined undefined undefined undefined-undefined
[26]  
van der Schaar M(undefined)undefined undefined undefined undefined-undefined
[27]  
Shrutivandana S(undefined)undefined undefined undefined undefined-undefined
[28]  
Demosthenis T(undefined)undefined undefined undefined undefined-undefined
[29]  
Suh K(undefined)undefined undefined undefined undefined-undefined
[30]  
Tan B(undefined)undefined undefined undefined undefined-undefined