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.

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.

An Intelligent Video Surveillance System for Android Smart Phone

2013 ◽  
Vol 850-851 ◽  
pp. 884-888 ◽  
Author(s):  
Gang Yang ◽  
Xin Tan ◽  
Yong Rui Zhang
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Surveillance System ◽  
Smart Phone ◽  
Image Data ◽  
Gaussian Mixture ◽  
Moving Object ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Surveillance Technology

Video surveillance technology is playing an important role, and it is widely used in some fields. With the popularity of Android OS, it draws researchers attention to increase the development of video surveillance systems on the platform. This paper presents a smart real-time video surveillance system based on Android smart phone. This system detects moving object by using improved GMM (Gaussian Mixture Mode) algorithm, recognizes invading human with cascade classifier, processes image data with coder & decoder, transmits data over RTP (Real-time Transport Protocol). It also applies some methods to improve the accuracy of moving object detection and recognition, speed up recognition process. The experimental evidences show that it can realize real-time video surveillance and smart alarm.

Real-Time Detection of Movement in Prohibited Direction for Video Surveillance System

2012 ◽  
Vol 17 (4) ◽  
pp. 251-264
Author(s):  
Mateusz Komorkiewicz
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Surveillance System ◽  
Detection Rate ◽  
Red Light ◽  
Surveillance Systems ◽  
Fast Reaction ◽  
Direction Of Movement ◽  
Stop And Search ◽  
Good Detection

Abstract Video surveillance systems are well established tools for monitoring important areas and detecting abnormal situations. In places such as one way road or tunnel, airport arrival gate, subway entry gate etc. it is important to monitor the direction of movement and to detect those which are prohibited. If the event is detected in the same time when the situation happens, a fast reaction can fix the problem (turning on the red light to prevent cars from entering the tunnel, sending security force to stop and search the suspect etc.). In the article a working system which is able to detect movement in prohibited direction is presented. The algorithm proved a very good detection rate for tested movie sequences. By optimizing various aspects of the algorithm a real-time efficiency (30fps) for 640×480 resolution frames is achieved.

scholarly journals A study on implementation of real-time intelligent video surveillance system based on embedded module

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jin Su Kim ◽  
Min-Gu Kim ◽  
Sung Bum Pan
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Surveillance System ◽  
Fire Detection ◽  
Fall Detection ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Intelligent Video Surveillance ◽  
Intruder Detection ◽  
Intelligent Video Surveillance System

AbstractConventional surveillance systems for preventing accidents and incidents do not identify 95% thereof after 22 min when one person monitors a plurality of closed circuit televisions (CCTV). To address this issue, while computer-based intelligent video surveillance systems have been studied to notify users of abnormal situations when they happen, it is not commonly used in real environment because of weakness of personal information leaks and high power consumption. To address this issue, intelligent video surveillance systems based on small devices have been studied. This paper suggests implement an intelligent video surveillance system based on embedded modules for intruder detection based on information learning, fire detection based on color and motion information, and loitering and fall detection based on human body motion. Moreover, an algorithm and an embedded module optimization method are applied for real-time processing. The implemented algorithm showed performance of 88.51% for intruder detection, 92.63% for fire detection, 80% for loitering detection and 93.54% for fall detection. The result of comparison before and after optimization about the algorithm processing time showed 50.53% of decrease, implying potential real-time driving of the intelligent image monitoring system based on embedded modules.

scholarly journals Secure Video Surveillance Framework in Smart City

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4419
Author(s):  
Hao Li ◽  
Tianhao Xiezhang ◽  
Cheng Yang ◽  
Lianbing Deng ◽  
Peng Yi
Keyword(s):  
Video Surveillance ◽  
Smart City ◽  
Key Management ◽  
Surveillance System ◽  
Chinese Remainder Theorem ◽  
Smart Cities ◽  
Group Key Management ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Key Update

In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.

Sign in / Sign up

Export Citation Format

Share Document