Study the Fisheye Staring Video Surveillance System of nothing Blind-Zone Based on the DaVinci Chip

2013 ◽  
Vol 846-847 ◽  
pp. 574-577
Author(s):  
Jian Hui Wu ◽  
Guo Yun Zhang ◽  
Long Yuan Guo ◽  
Shuai Yuan
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Imaging System ◽  
Calibration Method ◽  
Eye Lens ◽  
Video Surveillance System ◽  
Blind Zone ◽  
Hardware System ◽  
Imagery Processing ◽  
Fish Eye

A staring and nothing blind-zone video surveillance system was designed based on the DSP chip of TMS320DM6467 and the fish-eye lens which has ultra-view field. This fisheye lens has 185 degree field of view, and make the system has 360 degree panorama and nothing blind-zone monitoring which used two fish-eye lens imaging system. Firstly, this paper designed the panorama imaging system, and then studied the calibration method for it. The hardware system used the multimedia imagery processing chip of TMS320DM6467 which has dual core processor and can processing the fish-eye image real time. The experiment shows the fisheye image had 360 degree sphere with nothing blind-zone for surveillance area and the hardware of processing center can work stable. This system can intelligent surveillance when upload the algorithm.

Research on the Substation 3D Real Scene Surveillance

2014 ◽  
Vol 1008-1009 ◽  
pp. 742-747 ◽  
Author(s):  
Hao Chen ◽  
Ya Ping Hu ◽  
Jin Gyi Yan ◽  
Jiong Cong Chen ◽  
Juan Hu
Keyword(s):  
Video Surveillance ◽  
Camera Calibration ◽  
Surveillance System ◽  
Calibration Method ◽  
Video Monitoring ◽  
Video Surveillance System ◽  
Monitoring Point ◽  
Technical Problems ◽  
Real Scene ◽  
Calibration Algorithm

The reasonable distribution of the video monitoring points in the substation is one of the technical problems to be solved in the engineering. We combined the camera calibration algorithm with the real application of the substation video monitoring point layout, and proposed a nonlinear camera calibration method of the external parameters based on the 3D real scene, which is suited for the substation video surveillance system. The optimal video monitoring points are determined by the relationship between the video produced by the camera movement and the visual scope displayed with the 3D real scene, and testified by the location of each object through its boundary geometric element shown in the pictures. It has been proved applicable by the video simulating analysis on the 110kV main transformer monitoring points. This method has been used in the engineering design of the Guangdong Power Grid substation video surveillance system.

A Video Surveillance System for Monitoring the Endangered Mediterranean Monk Seal (Monachus monachus)

2007 ◽  
Vol 33 (2) ◽  
pp. 179-184 ◽  
Author(s):  
Panagiotis Dendrinos ◽  
Eleni Tounta ◽  
Alexandros A. Karamanlidis ◽  
Anastasios Legakis ◽  
Spyros Kotomatas
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System

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.

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.

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