scholarly journals Application Research of Key Frames Extraction Technology Combined with Optimized Faster R-CNN Algorithm in Traffic Video Analysis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhi-guang Jiang ◽  
Xiao-tian Shi
Keyword(s):  
Video Analysis ◽  
Management System ◽  
Large Scale ◽  
Data Communication ◽  
Transportation System ◽  
Extraction Technology ◽  
Transportation Management ◽  
Key Frame Extraction ◽  
Original Algorithm ◽  
Key Frame

The intelligent transportation system under the big data environment is the development direction of the future transportation system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology and applies them to the entire ground transportation management system to establish a real-time, accurate, and efficient comprehensive transportation management system that works on a large scale and in all directions. Intelligent video analysis is an important part of smart transportation. In order to improve the accuracy and time efficiency of video retrieval schemes and recognition schemes, this article firstly proposes a segmentation and key frame extraction method for video behavior recognition, using a multi-time scale dual-stream network to extract video features, improving the efficiency and efficiency of video behavior detection. On this basis, an improved algorithm for vehicle detection based on Faster R-CNN is proposed, and the Faster R-CNN network feature extraction layer is improved by using the principle of residual network, and a hole convolution is added to the network to filter out the redundant features of high-resolution video images to improve the problem of vehicle missed detection in the original algorithm. The experimental results show that the key frame extraction technology combined with the optimized Faster R-CNN algorithm model greatly improves the accuracy of detection and reduces the leakage. The detection rate is satisfactory.

The Research of Key Frame Extraction Technique Based on Video Content

2011 ◽  
Vol 474-476 ◽  
pp. 760-763
Author(s):  
Wei Zhe
Keyword(s):  
Video Analysis ◽  
Video Retrieval ◽  
Extraction Technique ◽  
Video Content ◽  
Extraction Techniques ◽  
Extraction Technology ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Typical Method ◽  
Made In

Key frame extraction is the precondition and fundamental of the video retrieval and video analysis. The method of key frame extraction determines the final result of video analysis. Firstly, the paper introduced the basic theories and principles of the Key frame extraction technology. Secondly, examined the typical method of key frame extraction techniques of video based on non-compression domain and compression-domain in detailed, at the same time, the evaluation and comparisons of the methods are made by experimental. Thirdly, the summarization is given and the prospection of Key frame extraction in the future is made in the end of paper.

scholarly journals Large-Scale Video Retrieval via Deep Local Convolutional Features

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chen Zhang ◽  
Bin Hu ◽  
Yucong Suo ◽  
Zhiqiang Zou ◽  
Yimu Ji
Keyword(s):  
Large Scale ◽  
Video Retrieval ◽  
Video Data ◽  
Query Image ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Storage Cost ◽  
Extraction Algorithm ◽  
Feature Aggregation ◽  
And Storage

In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.

scholarly journals Cloud and Edge Computing for Developing Smart Factory Models using a iFogSim Wrapper: Transportation Management System (TMS) Case Study

2020 ◽  
Author(s):  
Dhairya Patel ◽  
Sabah Mohammed
Keyword(s):  
Data Storage ◽  
Management System ◽  
Large Scale ◽  
Edge Computing ◽  
Smart Factory ◽  
Transportation Management ◽  
Use Of Resources ◽  
Active User ◽  
Processing Power ◽  
Transportation Simulation

<p><b>The given literature focuses on developing a Smart Factory model based on Cloud and Edge computing used to develop Transportation Management System(TMS) using a iFogSim wrapper. Cloud computing identifies data centres for users and offer computer system services on-demand, including data storage and processing power, without direct active user management. In the smart industry, several devices are connected together across the internet, where vast volumes of data are collected during the entire process of output. Thus, to handle this data smart factory based on cloud and edge computing is used. The intelligent cloud-based factory offers some facility like large scale analysis of data. Concepts like fog and edge computing play a significant role in extending data storage and network capacities in the cloud that addresses some challenges, such as over-full bandwidth and latency. The literature also focuses on the implementation of TMS using the iFogSim Simulator. The simulator provides efficient execution of TMS by showing the amount of resources used which gives an idea regarding optimum use of resources. All types of data related to TMS is obtained at cloud by using smart factory like object location, time taken and energy consumption. To implement the TMS we have created a topology which displays various devices connected to the cloud which gives necessary information regarding the ongoing transportation simulation.</b></p>

scholarly journals Cloud and Edge Computing for Developing Smart Factory Models using a iFogSim Wrapper: Transportation Management System (TMS) Case Study

2020 ◽  
Author(s):  
Dhairya Patel ◽  
Sabah Mohammed
Keyword(s):  
Data Storage ◽  
Management System ◽  
Large Scale ◽  
Edge Computing ◽  
Smart Factory ◽  
Transportation Management ◽  
Use Of Resources ◽  
Active User ◽  
Processing Power ◽  
Transportation Simulation

<p><b>The given literature focuses on developing a Smart Factory model based on Cloud and Edge computing used to develop Transportation Management System(TMS) using a iFogSim wrapper. Cloud computing identifies data centres for users and offer computer system services on-demand, including data storage and processing power, without direct active user management. In the smart industry, several devices are connected together across the internet, where vast volumes of data are collected during the entire process of output. Thus, to handle this data smart factory based on cloud and edge computing is used. The intelligent cloud-based factory offers some facility like large scale analysis of data. Concepts like fog and edge computing play a significant role in extending data storage and network capacities in the cloud that addresses some challenges, such as over-full bandwidth and latency. The literature also focuses on the implementation of TMS using the iFogSim Simulator. The simulator provides efficient execution of TMS by showing the amount of resources used which gives an idea regarding optimum use of resources. All types of data related to TMS is obtained at cloud by using smart factory like object location, time taken and energy consumption. To implement the TMS we have created a topology which displays various devices connected to the cloud which gives necessary information regarding the ongoing transportation simulation.</b></p>

The Research of Hohhot in the Integration of Urban and Rural Public Transport Management System

2014 ◽  
Vol 568-570 ◽  
pp. 1889-1892
Author(s):  
Cheng Bing Li ◽  
Yi Zhang ◽  
Rui Xue Guo ◽  
Xi Lu Li
Keyword(s):  
Public Transport ◽  
Public Transportation ◽  
Traffic Management ◽  
Management System ◽  
Early Stage ◽  
Transportation System ◽  
Transportation Management ◽  
Existing Problems ◽  
Public Transportation System ◽  
Transport Management

At present, the integration of urban and rural public transportation Hohhot is at an early stage, the thesis mainly studies the management of the integration of urban and rural public transportation system, analyzes the existing problems of integration of urban and rural public transport management system, puts forward the major traffic management mode, in the presence of Hohhot itself, puts forward about the strategy for the development of the integration of urban and rural public transportation management system, through these proposals and Suggestions to promote the development of Hohhot urban and rural public transportation integration.

Research on real-time video key frame extraction technology based on clustering

2021 ◽  
Author(s):  
Nan Lin ◽  
Chang Xu ◽  
Yinan Xu ◽  
Jianhong Ma ◽  
Yangjie Cao ◽  
...  
Keyword(s):  
Real Time ◽  
Extraction Technology ◽  
Key Frame Extraction ◽  
Key Frame
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