Critical Video Surveillance and Identification of Human Behavior Analysis of ATM Security Systems

2016 ◽  
pp. 93-118
Author(s):  
M. Sivabalakrishnan ◽  
R. Menaka ◽  
S. Jeeva
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Background Subtraction ◽  
Surveillance System ◽  
Security Systems ◽  
Research Issues ◽  
Background Estimation ◽  
Security Officers ◽  
Abnormal Behaviors ◽  
Human Behavior Analysis

Video surveillance cameras are placed in many places such as bank, hospital, toll gates, airports, etc. To take advantage of the video in real time, a human must monitor the system continuously in order to alert security officers if there is an emergency. Besides, for event detection a person can observe four cameras with good accuracy at a time. Therefore, this requires expensive human resources for real time video surveillance using current technology. The trajectory of one or more targets obtains for object tracking while recording above space and time. By tracking various objects, the burden of detection by human sentinels is greatly alleviated. Efficient and reliable automatic alarm system is useful for many ATM surveillance applications. ATM Video monitoring systems present many challenging research issues in human abnormal behaviors detection approaches. The framework of ATM video surveillance system encompassing various factors, such as image acquisition, background estimation, background subtraction, segmentation, people counting and tracking are briefly discussed in this chapter.

Critical Video Surveillance and Identification of Human Behavior Analysis of ATM Security Systems

2019 ◽  
pp. 315-341
Author(s):  
M. Sivabalakrishnan ◽  
R. Menaka ◽  
S. Jeeva
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Background Subtraction ◽  
Surveillance System ◽  
Security Systems ◽  
Research Issues ◽  
Background Estimation ◽  
Security Officers ◽  
Abnormal Behaviors ◽  
Human Behavior Analysis

Video surveillance cameras are placed in many places such as bank, hospital, toll gates, airports, etc. To take advantage of the video in real time, a human must monitor the system continuously in order to alert security officers if there is an emergency. Besides, for event detection a person can observe four cameras with good accuracy at a time. Therefore, this requires expensive human resources for real time video surveillance using current technology. The trajectory of one or more targets obtains for object tracking while recording above space and time. By tracking various objects, the burden of detection by human sentinels is greatly alleviated. Efficient and reliable automatic alarm system is useful for many ATM surveillance applications. ATM Video monitoring systems present many challenging research issues in human abnormal behaviors detection approaches. The framework of ATM video surveillance system encompassing various factors, such as image acquisition, background estimation, background subtraction, segmentation, people counting and tracking are briefly discussed in this chapter.

Smart Video Surveillance Systems and Identification of Human Behavior Analysis

2019 ◽  
pp. 64-97
Author(s):  
M. Sivabalakrishnan ◽  
R. Menaka ◽  
S. Jeeva
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Background Subtraction ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Cloud Environment ◽  
Background Estimation ◽  
Security Officers ◽  
Smart Surveillance ◽  
Human Behavior Analysis

Smart surveillance cameras are placed in many places such as bank, hospital, toll gates, airports, etc. To take advantage of the video in real time, a human must monitor the system continuously in order to alert security officers if there is an emergency. Besides, for event detection a person can observe four cameras with good accuracy at a time. Therefore, this requires expensive human resources for real-time video surveillance using current technology. The framework of ATM video surveillance system encompassing various factors, such as image acquisition, background estimation, background subtraction, store, and further process like segmentation, people counting, and tracking are done in cloud environment briefly discussed in this chapter.

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.

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