scholarly journals A Robust Watermarking Method for an Authentication of Video Surveillance Applications

2019 ◽  
Vol 9 (1S3) ◽  
pp. 500-506
Keyword(s):  
Video Surveillance ◽  
Wavelet Coefficient ◽  
Research Work ◽  
Digital Watermark ◽  
Video Watermarking ◽  
Discrete Wavelet ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Video Frames ◽  
Surveillance Applications

The problem of authenticating the video content is the major role of any automated video surveillance systems (AVS). This research work delivers an approach to authenticate the surveillance video which can be used by attorneys to prove their clients using Digital Watermarking. The digital watermark gives an assurance that the provided video surveillance frame is not tampered. In this paper, we proposed a robust video watermarking approach for an authentication of video surveillance applications. The digital watermark is embedded into the Discrete Wavelet domain of the identified key frames using holoentropy. Here, the pixel map will be optimallygenerated for the watermarking and the image embedding using the proposed classifier using Moth – flame - Rider optimization based Neural Network (MF-ROA-based NN) will generate the optimal map prediction based on the fitness measure, such as wavelet coefficient, energy, entropy, loop coefficient, and standard deviation, respectively. This optimal map is used for both embedding and extraction. The experimental results proves that the proposed systems can authenticate the surveillance video frames against various attacks when compared to the existing systems.

Privacy Preserving in Video Surveillance Systems Using Regression Residual Convolutional Neural Network in Private and Public Places

2020 ◽  
Vol 12 (1) ◽  
pp. 39-55
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Security Policy ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Public Places ◽  
Private And Public ◽  
The One ◽  
Regression Residual

In recent years, surveillance video has become a familiar phenomenon because it gives us a feeling of greater security, but we are continuously filmed and our privacy is greatly affected. This work deals with the development of a private video surveillance system (PVSS) using regression residual convolutional neural network (RR-CNN) with the goal to propose a new security policy to ensure the privacy of no-dangerous person and prevent crime. The goal is to best meet the interests of all parties: the one who films and the one who is filmed.

scholarly journals Performance Evaluation of Block based Video Watermarking after Attacks

2019 ◽  
Vol 8 (2S11) ◽  
pp. 225-233
Keyword(s):  
Performance Evaluation ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Digital Media ◽  
Video Watermarking ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Video Frames ◽  
Block Based

In today’s era use of digital media is most popular way of communication. Digital media covers images, videos and animations available online. The easy methods of accessing, copying and editing digital media have made them more popular. With several advantages these easy methods of copying and editing data have created some big issues like ownership identification. This increases the demand of protecting online digital media. Watermarking is solution of such problem. In this work, a block-based method has been proposed for video watermarking that uses a key at the time of embedding and extraction. Some frames are selected from the video according to a key. Watermark is embedded on the selected frames after dividing into parts called blocks. Each part of the watermark is embedded in one selected frame of the video. This method increases the security of the system as the complete watermark cannot be extracted without knowing the positions of watermarked frames and the position of the block in that frame. Watermarking is performed in the Discrete Wavelet Transform domain after scaling of watermark data. To show the authenticity of proposed scheme various attacks are applied on different watermarked video frames and extracted watermark results are shown under different tables.

scholarly journals Interesting Video Frames Capturing on Digital Video Development Platform

2011 ◽  
pp. 17-20
Author(s):  
D. Nethaji ◽  
Mary Joans ◽  
Mrs. S. J. Grace Shoba
Keyword(s):  
Video Surveillance ◽  
Field Of View ◽  
Surveillance Systems ◽  
Development Platform ◽  
Video Images ◽  
Video Frames ◽  
Data Rates ◽  
Video Processor ◽  
Human Monitoring

Video surveillance has been a popular security tool for years. Video surveillance systems produce huge amounts of data for storage and display. Long-term human monitoring of the acquired video is impractical and in-effective. This paper presents a novel solution for real-time cases that identify and record only “interesting” video frames containing motion. In addition to traditional methods for compressing individual video images, we could identify and record only “interesting” video images, such as those images with significant amounts of motion in the field of view. The model would be built in simulink, one of tools in matlab and incorporated with davinci code processor, a video processor. That could significantly help reduce the data rates for surveillance-specific applications.

A Survey of Visual Traffic Surveillance Using Spatio-Temporal Analysis and Mining

2013 ◽  
Vol 4 (3) ◽  
pp. 42-60 ◽  
Author(s):  
Chengcui Zhang
Keyword(s):  
Data Mining ◽  
Volume Estimation ◽  
Surveillance Systems ◽  
Temporal Data Mining ◽  
Temporal Data ◽  
Surveillance Video ◽  
Traffic Surveillance ◽  
Surveillance Videos ◽  
Spatio Temporal ◽  
Surveillance Applications

The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effectively analyze these spatio-temporal data to automatically find its inherent characteristics for different visual traffic surveillance applications has been of great interest. Examples of spatio-temporal patterns extracted from traffic surveillance videos include, but are not limited to, sudden stops, harsh turns, speeding, and collisions. To meet the different needs of various traffic surveillance applications, several application- or event- specific models have been proposed in the literature. This paper provides a survey of different models and data mining algorithms to cover state of the art in spatio-temporal modelling, spatio-temporal data mining, and spatio-temporal retrieval for traffic surveillance video databases. In addition, the database model issues and challenges for traffic surveillance videos are also discussed in this survey.

Collaborative Video Surveillance for Distributed Visual Data Mining of Potential Risk and Crime Detection

2011 ◽  
pp. 194-204 ◽  
Author(s):  
Chia-Hui Wang ◽  
Ray-I Chang ◽  
Jan-Ming Ho
Keyword(s):  
Data Mining ◽  
Video Surveillance ◽  
Potential Risk ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Visual Data ◽  
Visual Data Mining ◽  
Technology Advancement ◽  
Crime Detection ◽  
Collaborative Video

Thanks to fast technology advancement of micro-electronics, wired/wireless networks and computer computations in past few years, the development of intelligent, versatile and complicated video-based surveillance systems has been very active in both research and industry to effectively enhance safety and security. In this chapter, the authors first introduce the generations of video surveillance systems and their applications in potential risk and crime detection. For effectively supporting early warning system of potential risk and crime (which is load-heavy and time-critical), both collaborative video surveillance and distributed visual data mining are necessary. Moreover, as the surveillance video and data for safety and security are very important for all kinds of risk and crime detection, the system is required not only to data protection of the message transmission over Internet, but also to further provide reliable transmission to preserve the visual quality-of-service (QoS). As cloud computing, users do not need to own the physical infrastructure, platform, or software. They consume resources as a service, where Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), and pay only for resources that they use. Therefore, the design and implementation of an effective communication model is very important to this application system.

scholarly journals An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1559 ◽  
Author(s):  
Fan Zhang ◽  
Zhichao Xu ◽  
Wei Chen ◽  
Zizhe Zhang ◽  
Hao Zhong ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Discrete Wavelet Transform ◽  
Video Surveillance ◽  
Structural Similarity ◽  
Underground Mines ◽  
Noise Robustness ◽  
Discrete Wavelet ◽  
Surveillance Systems ◽  
Compression Method

Video surveillance systems play an important role in underground mines. Providing clear surveillance images is the fundamental basis for safe mining and disaster alarming. It is of significance to investigate image compression methods since the underground wireless channels only allow low transmission bandwidth. In this paper, we propose a new image compression method based on residual networks and discrete wavelet transform (DWT) to solve the image compression problem. The residual networks are used to compose the codec network. Further, we propose a novel loss function named discrete wavelet similarity (DW-SSIM) loss to train the network. Because the information of edges in the image is exposed through DWT coefficients, the proposed network can learn to preserve the edges better. Experiments show that the proposed method has an edge over the methods being compared in regards to the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), particularly at low compression ratios. Tests on noise-contaminated images also demonstrate the noise robustness of the proposed method. Our main contribution is that the proposed method is able to compress images at relatively low compression ratios while still preserving sharp edges, which suits the harsh wireless communication environment in underground mines.

The Analysis and Review of Mobile Surveillance Video Based on AVS-S

2012 ◽  
Vol 546-547 ◽  
pp. 634-639
Author(s):  
Wei Bing Chen ◽  
Fei Jang Huang ◽  
Gang Lin Zhang ◽  
Zhu Xian Zhang
Keyword(s):  
Video Compression ◽  
Video Surveillance ◽  
Surveillance Systems ◽  
Mobile Video ◽  
Surveillance Video ◽  
Video Technology ◽  
Security Monitoring ◽  
Development History ◽  
Technical Features ◽  
Audio Video

In order to realize digitizing and networking video surveillance, a kind of excellent technology of video compression is need. In this paper, the audio video coding standard based on AVS-S was systematically introduced, including its development history, target requirements, technical features and existing algorithms. All possible facing problems when AVS-S is applied in mobile video surveillance systems were analyzed. The corresponding solutions were also provided. Especially, how to use intelligent video technology to satisfy with even higher security-monitoring targets was discussed.

High-level surveillance event detection

2017 ◽  
Vol 01 (01) ◽  
pp. 1630007
Author(s):  
Fabio Persia ◽  
Daniela D’Auria
Keyword(s):  
Video Surveillance ◽  
Event Detection ◽  
Terrorist Attacks ◽  
Surveillance Systems ◽  
Detection Systems ◽  
Whole Process ◽  
Video Frames ◽  
The World ◽  
Public Areas ◽  
High Level

Security has been raised at major public buildings in the most famous and crowded cities all over the world following the terrorist attacks of the last years, the latest one at the Promenade des Anglais in Nice. For that reason, video surveillance systems have become more and more essential for detecting and hopefully even prevent dangerous events in public areas. In this work, we present an overview of the evolution of high-level surveillance event detection systems along with a prototype for anomaly detection in video surveillance context. The whole process is described, starting from the video frames captured by sensors/cameras till at the end some well-known reasoning algorithms for finding potentially dangerous activities are applied.

scholarly journals Rain Streak Removal for Single Images Using Conditional Generative Adversarial Networks

2021 ◽  
Vol 11 (5) ◽  
pp. 2214
Author(s):  
Prasad Hettiarachchi ◽  
Rashmika Nawaratne ◽  
Damminda Alahakoon ◽  
Daswin De Silva ◽  
Naveen Chilamkurti
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Smart City ◽  
Motion Blur ◽  
Generative Adversarial Networks ◽  
Great Success ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Deep Convolutional Neural Networks ◽  
Adversarial Networks

Rapid developments in urbanization and smart city environments have accelerated the need to deliver safe, sustainable, and effective resource utilization and service provision and have thereby enhanced the need for intelligent, real-time video surveillance. Recent advances in machine learning and deep learning have the capability to detect and localize salient objects in surveillance video streams; however, several practical issues remain unaddressed, such as diverse weather conditions, recording conditions, and motion blur. In this context, image de-raining is an important issue that has been investigated extensively in recent years to provide accurate and quality surveillance in the smart city domain. Existing deep convolutional neural networks have obtained great success in image translation and other computer vision tasks; however, image de-raining is ill posed and has not been addressed in real-time, intelligent video surveillance systems. In this work, we propose to utilize the generative capabilities of recently introduced conditional generative adversarial networks (cGANs) as an image de-raining approach. We utilize the adversarial loss in GANs that provides an additional component to the loss function, which in turn regulates the final output and helps to yield better results. Experiments on both real and synthetic data show that the proposed method outperforms most of the existing state-of-the-art models in terms of quantitative evaluations and visual appearance.

Collaborative Video Surveillance for Distributed Visual Data Mining of Potential Risk and Crime Detection

2012 ◽  
pp. 713-724
Author(s):  
Chia-Hui Wang ◽  
Ray-I Chang ◽  
Jan-Ming Ho
Keyword(s):  
Data Mining ◽  
Video Surveillance ◽  
Potential Risk ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Visual Data ◽  
Visual Data Mining ◽  
Technology Advancement ◽  
Crime Detection ◽  
Collaborative Video

Thanks to fast technology advancement of micro-electronics, wired/wireless networks and computer computations in past few years, the development of intelligent, versatile and complicated video-based surveillance systems has been very active in both research and industry to effectively enhance safety and security. In this chapter, the authors first introduce the generations of video surveillance systems and their applications in potential risk and crime detection. For effectively supporting early warning system of potential risk and crime (which is load-heavy and time-critical), both collaborative video surveillance and distributed visual data mining are necessary. Moreover, as the surveillance video and data for safety and security are very important for all kinds of risk and crime detection, the system is required not only to data protection of the message transmission over Internet, but also to further provide reliable transmission to preserve the visual quality-of-service (QoS). As cloud computing, users do not need to own the physical infrastructure, platform, or software. They consume resources as a service, where Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), and pay only for resources that they use. Therefore, the design and implementation of an effective communication model is very important to this application system.

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