scholarly journals A Video Surveillance System for Unmanned Surveillance of Cantonment Boundary

2020 ◽  
Vol 9 (8) ◽  
pp. 347-351
Keyword(s):  
Video Surveillance ◽  
Background Subtraction ◽  
Surveillance System ◽  
Human Detection ◽  
Video Surveillance System ◽  
Internal Security ◽  
Security Forces ◽  
Security Issues ◽  
Detection Approach ◽  
Day By Day

Since last few years, the Incidents that breach internal security and attack on the security forces are increasing day by day. These are security issues are becoming challenging to handle manually due to economical restrictions. This paper proposes an application for video surveillance to handle and monitor the intrusive incidents. The proposed application includes then human detection in no men’s land around the boundary of the army cantonment. The human detection approach is proposed in this paper is developed with integration of the object detection using background subtraction, feature extraction using CNN and object classification into human and non human using SVM. The proposed approach achieves 95.6% accuracy in human detection. Application proposed in this paper is useful for unmanned surveillance of cantonment boundary.

The Design and Implementation of Moving Object Detecting Algorithm in Intelligent Video Surveillance

2015 ◽  
Vol 738-739 ◽  
pp. 779-783
Author(s):  
Jin Hua Sun ◽  
Cui Hua Tian
Keyword(s):  
Video Surveillance ◽  
Background Subtraction ◽  
Surveillance System ◽  
Image Sequence ◽  
Background Modeling ◽  
Moving Object ◽  
Video Surveillance System ◽  
Surveillance Video ◽  
Subtraction Method ◽  
Design And Implementation

In view of the problems existed in moving object detection in video surveillance system, background subtraction method is adopted and combined with Surendra algorithm for background modeling, an algorithm of detecting moving object from video is proposed, and OpenCV programming is adopted in Visual c ++ 6.0 for implementation. Experimental results indicate that the algorithm can accurately detect and identify moving object in video by reading the image sequence of surveillance video, the validity of the algorithm is verified.

Human Detection by Surveillance Camera

2018 ◽  
Vol 6 (1) ◽  
pp. 21-33
Author(s):  
Larbi Guezouli ◽  
Hanane Boukhetache ◽  
Imene Kebi
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Video Sequence ◽  
Rapid Development ◽  
Human Detection ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Considerable Time ◽  
Surveillance Camera ◽  
Analytical Tools

Security problems and decreasing costs, leads to the rapid development of video surveillance systems. It is necessary to implement analytical tools capable of identifying objects that may appear in the video sequence. The work presented in this article consists of designing a video surveillance system for the automatic detection of humans in a video sequence acquired by a fixed camera. The principle of this work is based on the modeling and subtraction of the background. In order to determine the nature of the objects, the authors make the detection of the contours of the foreground image, then by matching this contour with the images of a base, silhouette images of people in different positions. The acquisition of the frames is carried out in real time, the matching of the images takes a considerable time and this time becomes increasingly longer based on the size of the base. To solve this problem, the authors have used the parallelism.

scholarly journals Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

2017 ◽  
Vol 18 (4) ◽  
pp. 297-306 ◽  
Author(s):  
Zakaria Moutakki ◽  
Imad Mohamed Ouloul ◽  
Karim Afdel ◽  
Abdellah Amghar
Keyword(s):  
Video Surveillance ◽  
Background Subtraction ◽  
Traffic Management ◽  
Surveillance System ◽  
Regions Of Interest ◽  
Vehicle Classification ◽  
Support Vector ◽  
Video Surveillance System ◽  
Occlusion Handling ◽  
Novel Method

Abstract The scope of this paper is a video surveillance system constituted of three principal modules, segmentation module, vehicle classification and vehicle counting. The segmentation is based on a background subtraction by using the Codebooks method. This step aims to define the regions of interest associated with vehicles. To classify vehicles in their type, our system uses the histograms of oriented gradient followed by support vector machine. Counting and tracking vehicles will be the last task to be performed. The presence of partial occlusion involves the decrease of the accuracy of vehicle segmentation and classification, which directly impacts the robustness of a video surveillance system. Therefore, a novel method to handle the partial occlusions based on vehicle classification process have developed. The results achieved have shown that the accuracy of vehicle counting and classification exceeds the accuracy measured in some existing systems.

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