An FPGA-Based Prototyping System for Real-Time Verification of Video Processing Schemes

1999 ◽  
pp. 333-338 ◽  
Author(s):  
Holger Kropp ◽  
Carsten Reuter ◽  
Matthias Wiege ◽  
Tien-Toan Do ◽  
Peter Pirsch
Keyword(s):  
Real Time ◽  
Video Processing

Design of a real-time video processing system with FPGA

2002 ◽  
Author(s):  
Wei Liu ◽  
Zeying Chi ◽  
Wenjian Chen
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Processing System

scholarly journals Energy-Efficient Multi- Core Scheduling for Real-Time Video Processing

2011 ◽  
Vol 16 (6) ◽  
pp. 11-20 ◽  
Author(s):  
Hyung-Goo Paek ◽  
Jeong-Mo Yeo ◽  
Wan-Yeon Lee
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Energy Efficient

scholarly journals Video Oculographic System using Real-Time Video Processing

2015 ◽  
Vol 119 (22) ◽  
pp. 15-18
Author(s):  
Akshat Gambhir ◽  
K.S Boppaiah ◽  
M Shruthi Subbaiah ◽  
Pooja M ◽  
Kiran P
Keyword(s):  
Real Time ◽  
Video Processing

Distributed Video Coding and Content Analysis for Resource Constraint Multimedia Applications

2012 ◽  
pp. 251-273
Author(s):  
Praveen Kumar ◽  
Amit Pande ◽  
Ankush Mittal ◽  
Abhisek Mudgal
Keyword(s):  
Video Coding ◽  
Real Time ◽  
Video Processing ◽  
Mobile Agents ◽  
Video Transmission ◽  
Moving Objects ◽  
Individual Case ◽  
Multimedia Applications ◽  
Real Time Processing ◽  
Laboratory Setup

Video coding and analysis for low power and low bandwidth multimedia applications has always been a great challenge. The limited computational resources on ubiquitous multimedia devices like cameras along with low and varying bandwidth over wireless network lead to serious bottlenecks in delivering real-time streaming of videos for such applications. This work presents a Content-based Network-adaptive Video-transmission (CbNaVt) framework which can waive off the requirements of low bandwidth. This is done by transmitting important content only to the end user. The framework is illustrated with the example of video streaming in the context of remote laboratory setup. A framework for distributed processing using mobile agents is discussed with the example of Distributed Video Surveillance (DVS). In this regard, the increased computational costs due to video processing tasks like object segmentation and tracking are shared by the cameras and a local base station called as Processing Proxy Server (PPS).However, in a distributed scenario like traffic surveillance, where moving objects is tracked using multiple cameras, the processing tasks needs to be dynamically distributed. This is done intelligently using mobile agents by migrating from one PPS to another for tracking an individual case object and transmitting required information to the end users. Although the authors propose a specific implementation for CbNaVt and DVS systems, the general ideas in design of such systems exemplify the way information can be intelligently transmitted in any ubiquitous multimedia applications along with the use of mobile agents for real-time processing and retrieval of video signal.

scholarly journals SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 911 ◽  
Author(s):  
Md Azher Uddin ◽  
Aftab Alam ◽  
Nguyen Anh Tu ◽  
Md Siyamul Islam ◽  
Young-Koo Lee
Keyword(s):  
Distributed Computing ◽  
Real Time ◽  
Video Surveillance ◽  
Video Processing ◽  
Large Scale ◽  
Distributed Processing ◽  
Content Management ◽  
Video Data ◽  
Video Analytics ◽  
Intelligent Video Surveillance

In recent years, the amount of intelligent CCTV cameras installed in public places for surveillance has increased enormously and as a result, a large amount of video data is produced every moment. Due to this situation, there is an increasing request for the distributed processing of large-scale video data. In an intelligent video analytics platform, a submitted unstructured video undergoes through several multidisciplinary algorithms with the aim of extracting insights and making them searchable and understandable for both human and machine. Video analytics have applications ranging from surveillance to video content management. In this context, various industrial and scholarly solutions exist. However, most of the existing solutions rely on a traditional client/server framework to perform face and object recognition while lacking the support for more complex application scenarios. Furthermore, these frameworks are rarely handled in a scalable manner using distributed computing. Besides, existing works do not provide any support for low-level distributed video processing APIs (Application Programming Interfaces). They also failed to address a complete service-oriented ecosystem to meet the growing demands of consumers, researchers and developers. In order to overcome these issues, in this paper, we propose a distributed video analytics framework for intelligent video surveillance known as SIAT. The proposed framework is able to process both the real-time video streams and batch video analytics. Each real-time stream also corresponds to batch processing data. Hence, this work correlates with the symmetry concept. Furthermore, we introduce a distributed video processing library on top of Spark. SIAT exploits state-of-the-art distributed computing technologies with the aim to ensure scalability, effectiveness and fault-tolerance. Lastly, we implant and evaluate our proposed framework with the goal to authenticate our claims.

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