Traffic Surveillance Video Summarization for Detecting Traffic Rules Violators Using R-CNN

2018 ◽  
pp. 117-126
Author(s):  
Veena Mayya ◽  
Aparna Nayak
Keyword(s):  
Video Summarization ◽  
Surveillance Video ◽  
Traffic Surveillance ◽  
Traffic Rules

scholarly journals Traffic Surveillance Video Management System

2010 ◽  
Vol 2 (4) ◽  
pp. 28-36
Author(s):  
K Ganesan ◽  
C Kavitha ◽  
Kriti Tandon ◽  
R Lakshmipriya
Keyword(s):  
Management System ◽  
Surveillance Video ◽  
Traffic Surveillance

Selecting attentive frames from visually coherent video chunks for surveillance video summarization

2017 ◽  
Author(s):  
Wenzhong Wang ◽  
Qiaoqiao Zhang ◽  
Bin Luo ◽  
Jin Tang ◽  
Rui Ruan ◽  
...  
Keyword(s):  
Video Summarization ◽  
Surveillance Video

scholarly journals Grassmann Manifold Based State Analysis Method of Traffic Surveillance Video

2019 ◽  
Vol 9 (7) ◽  
pp. 1319 ◽  
Author(s):  
Peng Qin ◽  
Yong Zhang ◽  
Boyue Wang ◽  
Yongli Hu
Keyword(s):  
Euclidean Space ◽  
Grassmann Manifold ◽  
Rapid Development ◽  
Video Data ◽  
High Dimensional ◽  
Intelligent Transport System ◽  
Surveillance Video ◽  
Traffic Surveillance ◽  
Traditional Methods ◽  
State Analysis

For a contemporary intelligent transport system, congestion state analysis of traffic surveillance video (TSV) is one of the most crucial and intricate research topics because of the rapid development of transportation systems, the sustained growth of surveillance facilities on road, which lead to massive traffic flow data, and the inherent characteristics of our analysis target. Traditional methods on feature extractions are usually operated on Euclidean space in general, which are not accurate for high-dimensional TSV data analysis. This paper proposes a Grassmann manifold based neural network model to analysis TSV data , by mapping the video data from high dimensional Euclidean space to Grassmann manifold space, and considering the inner relation among adjacent cameras. The accuracy of the traffic congestion is improved, compared with several traditional methods. Experimental results are conducted to validate the accuracy of our method and to investigate the effects of different factors on performance.

Traffic surveillance video coding with libraries of vehicles and background

2019 ◽  
Vol 60 ◽  
pp. 426-440 ◽  
Author(s):  
Changyue Ma ◽  
Dong Liu ◽  
Xiulian Peng ◽  
Li Li ◽  
Feng Wu
Keyword(s):  
Video Coding ◽  
Surveillance Video ◽  
Traffic Surveillance

The Detection of Beard Behavior of Taxi Drivers Based on Traffic Surveillance Video

2018 ◽  
pp. 653-661
Author(s):  
Zuyun Wang ◽  
Xunping Huang ◽  
Kebin Jia ◽  
Pengyu Liu ◽  
Zhonghua Sun
Keyword(s):  
Surveillance Video ◽  
Traffic Surveillance ◽  
Taxi Drivers
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