scholarly journals An aggregated deep convolutional recurrent model for event based surveillance video summarisation: A supervised approach

2021 ◽  
Author(s):  
Sreeja M. U. ◽  
Binsu C. Kovoor
Keyword(s):  
Surveillance Video ◽  
Event Based ◽  
Video Summarisation

scholarly journals Event-based large scale surveillance video summarization

2016 ◽  
Vol 187 ◽  
pp. 66-74 ◽  
Author(s):  
Xinhui Song ◽  
Li Sun ◽  
Jie Lei ◽  
Dapeng Tao ◽  
Guanhong Yuan ◽  
...  
Keyword(s):  
Large Scale ◽  
Video Summarization ◽  
Surveillance Video ◽  
Event Based

scholarly journals Flexible Human Behavior Analysis Framework for Video Surveillance Applications

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Weilun Lao ◽  
Jungong Han ◽  
Peter H. N. de With
Keyword(s):  
Behavior Analysis ◽  
Semantic Analysis ◽  
Human Motion ◽  
Video Sequences ◽  
Body Parts ◽  
Surveillance Video ◽  
Skeleton Model ◽  
Trajectory Estimation ◽  
Flexible Framework ◽  
Event Based

We study a flexible framework for semantic analysis of human motion from surveillance video. Successful trajectory estimation and human-body modeling facilitate the semantic analysis of human activities in video sequences. Although human motion is widely investigated, we have extended such research in three aspects. By adding a second camera, not only more reliable behavior analysis is possible, but it also enables to map the ongoing scene events onto a 3D setting to facilitate further semantic analysis. The second contribution is the introduction of a 3D reconstruction scheme for scene understanding. Thirdly, we perform a fast scheme to detect different body parts and generate a fitting skeleton model, without using the explicit assumption of upright body posture. The extension of multiple-view fusion improves the event-based semantic analysis by 15%–30%. Our proposed framework proves its effectiveness as it achieves a near real-time performance (13–15 frames/second and 6–8 frames/second) for monocular and two-view video sequences.

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