3-D COMPUTER VISION MODELING IN VIDEO SURVEILLANCE APPLICATIONS

2009 ◽  
pp. 747-765
Author(s):  
Giovanni B. Garibotto
Keyword(s):  
Computer Vision ◽  
Video Surveillance ◽  
Vision Modeling ◽  
Surveillance Applications

scholarly journals Smart Video Surveillance System Based on Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2958
Author(s):  
Antonio Carlos Cob-Parro ◽  
Cristina Losada-Gutiérrez ◽  
Marta Marrón-Romera ◽  
Alfredo Gardel-Vicente ◽  
Ignacio Bravo-Muñoz
Keyword(s):  
Computer Vision ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Smart Camera ◽  
People Detection ◽  
Camera System ◽  
Edge Node ◽  
New Processing ◽  
Processor Unit

New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people’s movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system.

scholarly journals Foreground Segmentation in Depth Imagery Using Depth and Spatial Dynamic Models for Video Surveillance Applications

Sensors ◽  
2014 ◽  
Vol 14 (2) ◽  
pp. 1961-1987 ◽  
Author(s):  
Carlos del-Blanco ◽  
Tomás Mantecón ◽  
Massimo Camplani ◽  
Fernando Jaureguizar ◽  
Luis Salgado ◽  
...  
Keyword(s):  
Video Surveillance ◽  
Dynamic Models ◽  
Foreground Segmentation ◽  
Spatial Dynamic ◽  
Surveillance Applications

A two-step face hallucination approach for video surveillance applications

2013 ◽  
Vol 74 (6) ◽  
pp. 1845-1862 ◽  
Author(s):  
Zhen Jia ◽  
Jianwei Zhao ◽  
Hongcheng Wang ◽  
Ziyou Xiong ◽  
Alan Finn
Keyword(s):  
Video Surveillance ◽  
Face Hallucination ◽  
Surveillance Applications

Security Applications Using Computer Vision

2013 ◽  
pp. 1093-1110
Author(s):  
Sreela Sasi
Keyword(s):  
Computer Vision ◽  
Power Plant ◽  
Video Surveillance ◽  
Homeland Security ◽  
Port Security ◽  
Video Cameras ◽  
Security Applications ◽  
Wide Range ◽  
Cargo Inspection ◽  
Computer Vision Systems

Computer vision plays a significant role in a wide range of homeland security applications. The homeland security applications include: port security (cargo inspection), facility security (embassy, power plant, bank), and surveillance (military or civilian), et cetera. Video surveillance cameras are placed in offices, hospitals, banks, ports, parking lots, parks, stadiums, malls, train stations, airports, et cetera. The challenge is not for acquiring surveillance data from these video cameras, but for identifying what is valuable, what can be ignored, and what demands immediate attention. Computer vision systems attempt to construct meaningful and explicit descriptions of the environment or scene captured in an image. A few Computer Vision based security applications are presented here for securing building facility, railroad (Objects on railroad, and red signal detection), and roads.

Bitstream-Based JPEG Encryption in Real-time

2013 ◽  
Vol 5 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Stefan Auer ◽  
Alexander Bliem ◽  
Dominik Engel ◽  
Andreas Uhl ◽  
Andreas Unterweger
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Time Operation ◽  
Real Time Operation ◽  
Surveillance Applications

The authors propose a framework to encrypt Baseline JPEG files directly at bitstream level, i.e., without the need to recompress them. The authors’ approach enables encrypting more than 25 pictures per second in VGA resolution, allowing real-time operation in typical video surveillance applications. In addition, their approach preserves the length of the bitstream while being completely format-compliant. Furthermore, the authors show that an attack on the encryption process, which partly relies on AES, is practically infeasible.

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