Design and Implementation of a Real-Time Video Stream Analysis System Based on FFMPEG

Li, Xiaohua; Jiang, Xiuhua; Wang, Caihong

doi:10.1109/wcse.2013.38

Cited by 35 publications

(6 citation statements)

References 1 publication

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“…Several of the videos also include the application of TikTok video filters which drastically changes the visual scene and the visual fidelity of the video by the introduction of noise, as shown in Figure 1 and Figure 2. For our deep learning classification experiment with FastAI, a set of frames of one-second intervals of each second in a video is extracted by using fast forward moving picture experts group (FFMPEG), an open-source multimedia framework [18]. This generated a total of 17,389 images from the videos that can be categorized into the respective dance class of the original video that the image was extracted from.…”

Section: Methodsmentioning

confidence: 99%

Classification of dances using AlexNet, ResNet18 and SqueezeNet1_0

Zakry

Hipiny

Ujir

2023

IJ-AI

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Dancing is an art form of creative expression that is based on movement. Dancing comprises varying styles, pacing and composition to convey an artist’s expression. Thus, the classification of any dance to a certain genre or type depends on how accurate or similar it is to what is generally understood to be the specific movements of that dance type. This presents a problem for new dancers to assess if the dance movements that they have just learned is accurate or not to what the original dance type is. This paper proposed that deep learning models can classify dance videos of amateur dancers according to the similar movements of actions of several dance classes. For this study, AlexNet, ResNet and SqueezeNet models was used to perform training on multiple frames of actions of several dance videos for label prediction and the classification accuracy of the models during each training epoch is compared. This study observed that the average classification accuracy of the deep learning models is 94.9669% and is comparable to other approaches used for dance classifications.

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Section: Methodsmentioning

confidence: 99%

Classification of dances using AlexNet, ResNet18 and SqueezeNet1_0

Zakry

Hipiny

Ujir

2023

IJ-AI

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“…In data centers, some servers run specific types of workflow, and some run multiple types of combined workflows. In order to be as close as possible to the actual data centers, we run three different types of tasks: World Cup 98 (WC98) benchmark, FFmpeg benchmark, 33 and a hybrid workflow. WC98 is a typical web-requested benchmark that the server parses the request from the client and sends the result to users.…”

Section: Data Acquisitionmentioning

confidence: 99%

Data characteristics aware prediction model for power consumption of data center servers

Shen

Zhou

Zhang

et al. 2022

Concurrency and Computation

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Summary Due to the rapid increase in the number and scale of data centers, the information and communication technology (ICT) equipment in data centers consumes an enormous amount of power. A power prediction model is therefore essential for decision‐making optimization and power management of ICT equipment. However, it is difficult to predict the power consumption of data centers accurately due to the complex power patterns and nonlinear interdependencies among components. Existing methods either rely on standard formulas, or simply treat it as time series, both leading to poor power prediction accuracy. To overcome those limitations, in this article, we present a systematic power prediction framework called characteristic aware attention‐augmented deep learning‐based prediction method. In particular, we first analyze the different power consumption series to illustrate their different temporal characteristics. Second, we perform different data processing for the corresponding characteristics of power series samples. Third, we propose an accurate and efficient neural network model to predict future power consumption with the pretreated data. The experimental results show that the proposed model is able to achieve superior prediction accuracy.

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“…Other auxiliary technologies are also present in the tool, such as the D3js visualization kernel [54], which assists in the development of visualizations for the web, and the Django framework [55], which facilitates the construction of interactive web applications. Additionally, the FFMPEG [56] and Pyaudio [57] tools assist in the manipulation and editing of video and audio files, respectively, to synchronize the various files submitted to the tool and to perform pre-processing operations such as silence removal.…”

Section: Technologiesmentioning

confidence: 99%

UXmood—A Sentiment Analysis and Information Visualization Tool to Support the Evaluation of Usability and User Experience

et al. 2019

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This paper presents UXmood, a tool that provides quantitative and qualitative information to assist researchers and practitioners in the evaluation of user experience and usability. The tool uses and combines data from video, audio, interaction logs and eye trackers, presenting them in a configurable dashboard on the web. The UXmood works analogously to a media player, in which evaluators can review the entire user interaction process, fast-forwarding irrelevant sections and rewinding specific interactions to repeat them if necessary. Besides, sentiment analysis techniques are applied to video, audio and transcribed text content to obtain insights on the user experience of participants. The main motivations to develop UXmood are to support joint analysis of usability and user experience, to use sentiment analysis for supporting qualitative analysis, to synchronize different types of data in the same dashboard and to allow the analysis of user interactions from any device with a web browser. We conducted a user study to assess the data communication efficiency of the visualizations, which provided insights on how to improve the dashboard.

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Design and Implementation of a Real-Time Video Stream Analysis System Based on FFMPEG

Cited by 35 publications

References 1 publication

Classification of dances using AlexNet, ResNet18 and SqueezeNet1_0

Classification of dances using AlexNet, ResNet18 and SqueezeNet1_0

Data characteristics aware prediction model for power consumption of data center servers

UXmood—A Sentiment Analysis and Information Visualization Tool to Support the Evaluation of Usability and User Experience

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