Fully pipelined VLSI architecture of a real-time block-based object detector for intelligent video surveillance systems

2015 ◽  
Author(s):  
Min-Chun Tuan ◽  
Shih-Lun Chen
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Vlsi Architecture ◽  
Surveillance Systems ◽  
Time Block ◽  
Intelligent Video Surveillance ◽  
Block Based

A Brief Cartography of Smart Cameras

2011 ◽  
pp. 257-271
Author(s):  
Fernanda Bruno
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Urban Space ◽  
Automated Detection ◽  
Surveillance Systems ◽  
Smart Cameras ◽  
Intelligent Video Surveillance ◽  
Individual Level ◽  
Future Events ◽  
Control Devices

This chapter carries out a brief cartography of the so-called “intelligent” video surveillance systems. These systems are programmed to accomplish real time automated detection of situations considered irregular and/or suspicious in specific environments, in order to predict and prevent undesirable events. Three aspects of the smart cameras are focused in this cartography. First, the author explores its regime of visibility and note how it prioritizes the capture of irregularities in the body’s movements in urban space. Second, the author shows how the type of monitoring and profiling of bodies and behaviors in these systems generally acts at the visible, surface and infra-individual level of human conduct. Finally, he analyzes the temporality of smart cameras, especially in its proactive dimension that intends to foresee and intervene, in real time, in future events. The analysis of these three aspects of the intelligent video surveillance identifies and highlights discourses, processes and operations that are common to the exercising of power and surveillance in contemporary societies – more specifically, those which are included in the realm of control devices.

A Brief Cartography of Smart Cameras

2013 ◽  
pp. 66-80
Author(s):  
Fernanda Bruno
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Urban Space ◽  
Automated Detection ◽  
Surveillance Systems ◽  
Smart Cameras ◽  
Intelligent Video Surveillance ◽  
Individual Level ◽  
Future Events ◽  
Control Devices

This chapter carries out a brief cartography of the so-called “intelligent” video surveillance systems. These systems are programmed to accomplish real time automated detection of situations considered irregular and/or suspicious in specific environments, in order to predict and prevent undesirable events. Three aspects of the smart cameras are focused in this cartography. First, the author explores its regime of visibility and note how it prioritizes the capture of irregularities in the body’s movements in urban space. Second, the author shows how the type of monitoring and profiling of bodies and behaviors in these systems generally acts at the visible, surface and infra-individual level of human conduct. Finally, he analyzes the temporality of smart cameras, especially in its proactive dimension that intends to foresee and intervene, in real time, in future events. The analysis of these three aspects of the intelligent video surveillance identifies and highlights discourses, processes and operations that are common to the exercising of power and surveillance in contemporary societies – more specifically, those which are included in the realm of control devices.

scholarly journals Algorithms for selecting the outlines of objects images in intellectual video surveillance systems

Connectivity ◽  
2020 ◽  
Vol 146 (5) ◽  
Author(s):  
L. P. Kriuchkova ◽  
◽  
V. I. Strelnikov ◽  
M. V. Akulinicheva ◽  
O. S. Bortnyk ◽  
...  
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Intelligent Systems ◽  
Surveillance Systems ◽  
Contour Analysis ◽  
Space Length ◽  
Intelligent Video Surveillance ◽  
Video Information ◽  
Wide Range ◽  
Selection Of

Intensive development of means of receiving and transmitting digital images creates the problem of processing huge amounts of video information flows. There is a wide range of tasks in which images are considered as a source of information on the basis of which it is necessary to make a decision. Important tasks to be solved by intelligent video surveillance systems are: identification of objects and determination of their trajectories; measuring the speed of objects; detection of alarming events in the tasks of object-territorial protection in real time. One of the main operations in intelligent video surveillance systems in image processing for further analysis is the selection of contours of images of objects, because the contour contains all the necessary information to recognize objects by their shape. This approach allows you to not consider the internal points of the image and, thus, significantly reduce the amount of information processed. This makes it possible to analyze images in real time. Contour analysis is a set of methods for selecting, describing and processing image contours that allows you to describe, store, compare and search for objects presented in the form of their external contours, as well as effectively solve the main problems of pattern recognition — transfer, rotate and zoom image of the object. In this case, the contour means a space-length gap, difference or abrupt change in brightness values. The purpose of the publication is to consider the algorithms for selecting the contours of images of objects in the problems of detecting alarming events by intelligent video surveillance systems. The problem of selection of contours of images of objects in problems of detection of disturbing events by intelligent systems of video surveillance is considered. In order to improve the basic characteristics of intelligent video surveillance systems, algorithms for contouring images of objects are proposed to ensure the detection of four types of alarming events: the appearance and presence of the object in the surveillance zone, moving the object in the forbidden direction, leaving the object and overturning the object.

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 SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 911 ◽  
Author(s):  
Md Azher Uddin ◽  
Aftab Alam ◽  
Nguyen Anh Tu ◽  
Md Siyamul Islam ◽  
Young-Koo Lee
Keyword(s):  
Distributed Computing ◽  
Real Time ◽  
Video Surveillance ◽  
Video Processing ◽  
Large Scale ◽  
Distributed Processing ◽  
Content Management ◽  
Video Data ◽  
Video Analytics ◽  
Intelligent Video Surveillance

In recent years, the amount of intelligent CCTV cameras installed in public places for surveillance has increased enormously and as a result, a large amount of video data is produced every moment. Due to this situation, there is an increasing request for the distributed processing of large-scale video data. In an intelligent video analytics platform, a submitted unstructured video undergoes through several multidisciplinary algorithms with the aim of extracting insights and making them searchable and understandable for both human and machine. Video analytics have applications ranging from surveillance to video content management. In this context, various industrial and scholarly solutions exist. However, most of the existing solutions rely on a traditional client/server framework to perform face and object recognition while lacking the support for more complex application scenarios. Furthermore, these frameworks are rarely handled in a scalable manner using distributed computing. Besides, existing works do not provide any support for low-level distributed video processing APIs (Application Programming Interfaces). They also failed to address a complete service-oriented ecosystem to meet the growing demands of consumers, researchers and developers. In order to overcome these issues, in this paper, we propose a distributed video analytics framework for intelligent video surveillance known as SIAT. The proposed framework is able to process both the real-time video streams and batch video analytics. Each real-time stream also corresponds to batch processing data. Hence, this work correlates with the symmetry concept. Furthermore, we introduce a distributed video processing library on top of Spark. SIAT exploits state-of-the-art distributed computing technologies with the aim to ensure scalability, effectiveness and fault-tolerance. Lastly, we implant and evaluate our proposed framework with the goal to authenticate our claims.

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