Quality-of-content (QoC)-driven rate allocation for video analysis in mobile surveillance networks

2015 ◽  
Author(s):  
Xiang Chen ◽  
Jenq-Neng Hwang ◽  
Kuan-Hui Lee ◽  
Ricardo L. de Queiroz
Keyword(s):  
Video Analysis ◽  
Rate Allocation ◽  
Surveillance Networks ◽  
Mobile Surveillance

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

From CCTV to Biometrics through Mobile Surveillance

2009 ◽  
pp. 1096-1102
Author(s):  
Jason Gallo
Keyword(s):  
Radio Frequency ◽  
Information And Communication Technologies ◽  
Radio Frequency Identification ◽  
Credit Card ◽  
Multiple Sources ◽  
Surveillance Networks ◽  
Geographic Space ◽  
Pass Through ◽  
The Individual ◽  
Mobile Surveillance

Surveillance is the act or process of observing, tracking, or recording personal details for the purpose of exercising control over the individual or population being watched. Control in this context can mean many things, from directly influencing the behavior of the observed to the use of gathered information for the purpose of management or governance. Mobile surveillance can be defined as two distinct, yet related, practices. The first is the ability to observe the physical movement of an individual through space. This is most often accomplished through documenting their interaction with a surveillance network. The object of surveillance is tracked from one node of the network to another, providing a record of behavior. The second practice is often referred to as dataveillance, or the ability to monitor an individual’s behavior through studying a trail of personally identifiable data, including credit card purchases, mobile phone calls, and health records. Mobile surveillance employs an array of technologies including video and photography cameras, visual recognition software, radio frequency identification (RFID), global positioning receivers (GPS), information and communication technologies (ICTs), and biometrics. Examples of mobile surveillance networks include the dense deployment of closed-circuit television (CCTV), video, and photographic technologies in a distinct geographic space to monitor activity, the tracking of automobiles and mobile phones via GPS, and radio frequency sensing that records motion as identity chips pass through a distributed network of receivers. As these networks proliferate, individuals are the exposed to overlapping layers of surveillance. Although many of these surveillance networks are deployed for limited purposes, the increasing ability to save and store personally identifiable information in searchable databases, and the ability to mine information from multiple sources raises privacy concerns for the individual. This is especially true in advanced capitalist societies that rely on sophisticated data gathering to track, model, and predict consumer behavior, as well as for citizen management.

The Impact of Real-Time Feedback and Video Analysis on Early Childhood Teachers’ Practice

2019 ◽  
pp. 027112141985714
Author(s):  
Christan Grygas Coogle ◽  
Sarah Nagro ◽  
Kelley Regan ◽  
Kristen Merrill O’Brien ◽  
Jennifer R. Ottley
Keyword(s):  
Professional Development ◽  
Real Time ◽  
Video Analysis ◽  
Single Case ◽  
Early Childhood Teachers ◽  
Single Case Research ◽  
Multiple Probe ◽  
The Impact ◽  
Intervention Condition

We used a multiple-probe single-case research design to examine the effect of a professional development package that included real-time technology-enhanced performance-based feedback and video analysis on three preschool teachers’ use of naturalistic instruction targeting children’s communication and child responses. We also measured the quality of the teachers’ naturalistic instruction targeting children’s communication. The professional development package was effective in enhancing teachers’ use of naturalistic instruction targeting children’s communication. In addition, children responded and teachers enhanced the quality of naturalistic instruction targeting children’s communication. Teachers maintained their use of naturalistic instruction targeting children’s communication upon removal of the intervention condition.

Analysing Quality of Resilience in Fish4Knowledge Video Analysis Workflows

2013 ◽  
Author(s):  
Gayathri Nadarajan ◽  
Cheng-Lin Yang ◽  
Yun-Heh Chen-Burger ◽  
Rafael Tolosana-Calasanz ◽  
Omer F. Rana
Keyword(s):  
Video Analysis

scholarly journals STUDY ON THE EVOLUTION OF THE ROMANIAN WOMEN’S EPEE TEAM AT THE OLYMPIC GAMES

2020 ◽  
pp. 187-196
Keyword(s):  
Literature Review ◽  
Success Rate ◽  
Detailed Analysis ◽  
Video Analysis ◽  
Olympic Games ◽  
Training Process ◽  
Recovery Times ◽  
Bibliographic Study ◽  
The Olympic Games

This study refers to the evolution of the Romanian Women’s Epee Team at the Olympic Games in terms of performance, but especially from the perspective of making up the team and establishing the strategy for addressing each goal-focused competition. The literature review reveals that one of the ways to increase both the quality of the training process in fencing and the success rate in team events lies in the team composition, but also in establishing the sequence of athletes competing in a match. Research purpose: The study aims to highlight the dynamics of the Romanian Women’s Epee Team through both a detailed analysis of the results achieved by athletes in their fight against various opponents and their comparison at different times of the matches. Methods: Bibliographic study, comparison, abstraction, induction and deduction methods, video analysis, mathematical and statistical methods. Results and conclusion: The sequence of athletes’ performance in team competitions and their effectiveness depending on the number of won/lost bouts, but also successful hits and recovery times were analysed based on the competition protocols for the Rio de Janeiro and London Olympics, and the performance of the Romanian Women’s Epee Team was compared with the performance achieved by other competing teams. The study has shown that, in today’s modern fencing (particularly the Epee), one of the most important factors for achieving performance in the team event refers to making up the team so that it can work as a whole in relation to the opposing team’s composition.

From CCTV to Biometrics through Mobile Surveillance

2008 ◽  
pp. 1158-1164
Author(s):  
Jason Gallo
Keyword(s):  
Radio Frequency ◽  
Information And Communication Technologies ◽  
Radio Frequency Identification ◽  
Credit Card ◽  
Multiple Sources ◽  
Surveillance Networks ◽  
Geographic Space ◽  
Pass Through ◽  
The Individual ◽  
Mobile Surveillance

Surveillance is the act or process of observing, tracking, or recording personal details for the purpose of exercising control over the individual or population being watched. Control in this context can mean many things, from directly influencing the behavior of the observed to the use of gathered information for the purpose of management or governance. Mobile surveillance can be defined as two distinct, yet related, practices. The first is the ability to observe the physical movement of an individual through space. This is most often accomplished through documenting their interaction with a surveillance network. The object of surveillance is tracked from one node of the network to another, providing a record of behavior. The second practice is often referred to as dataveillance, or the ability to monitor an individual’s behavior through studying a trail of personally identifiable data, including credit card purchases, mobile phone calls, and health records. Mobile surveillance employs an array of technologies including video and photography cameras, visual recognition software, radio frequency identification (RFID), global positioning receivers (GPS), information and communication technologies (ICTs), and biometrics. Examples of mobile surveillance networks include the dense deployment of closed-circuit television (CCTV), video, and photographic technologies in a distinct geographic space to monitor activity, the tracking of automobiles and mobile phones via GPS, and radio frequency sensing that records motion as identity chips pass through a distributed network of receivers. As these networks proliferate, individuals are the exposed to overlapping layers of surveillance. Although many of these surveillance networks are deployed for limited purposes, the increasing ability to save and store personally identifiable information in searchable databases, and the ability to mine information from multiple sources raises privacy concerns for the individual. This is especially true in advanced capitalist societies that rely on sophisticated data gathering to track, model, and predict consumer behavior, as well as for citizen management.

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