Design and Realization of IOT-Based Video Monitoring System

2014 ◽  
Vol 644-650 ◽  
pp. 3314-3317
Author(s):  
Jing Yin ◽  
Chuan Hua Li
Keyword(s):  
Monitoring System ◽  
Video Monitoring ◽  
Email Address ◽  
Web Camera ◽  
Image Monitoring

In this paper, an IOT video monitoring system is designed based on UP-Magic6410 platform, and web camera function within LAN and yeelink-based image monitoring within WAN are realized. In addition, dynamic images could be sent to designated email address after being coded to avi formatted video, which could be directly played to provide monitoring footage. Video and image could also be viewed and downloaded via ftp within LAN.

Design of the Embedded Video Image Monitoring System Based on B/S Mode

2012 ◽  
Vol 433-440 ◽  
pp. 5722-5726
Author(s):  
Qing Hui Wang ◽  
Hong Wei Chai
Keyword(s):  
Image Processing ◽  
Monitoring System ◽  
Video Compression ◽  
Image Acquisition ◽  
Compression Algorithm ◽  
Video Image ◽  
Video Monitoring ◽  
Image Monitoring ◽  
Implementation Scheme ◽  
The Web

Relying on powerful image processing capabilities and special parallel peripheral interface which is based on the ADI Corporation Blackfin561 DSP and using advanced H.264 video compression algorithm under the operate system of the uClinux. The implementation scheme of video image monitoring system based on B/S mode is proposed. By running the web boa server under the uClinux on the BF561 platform, the system realizes the video image acquisition,processing and network transmission.The result shows that the client can see the clearer images through the browser and the system meets the requirements of the remote video monitoring.

scholarly journals COVIMOS: A Coastal Video Monitoring System

2017 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
I Made Oka Widyantara ◽  
I Made Dwi Asana Putra ◽  
Ida Bagus Putu Adnyana
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Low Cost ◽  
Video Image ◽  
Video Monitoring ◽  
Reference Object ◽  
Self Organizing Map ◽  
K Nearest Neighbor ◽  
Ip Camera ◽  
Class Labels

This paper intends to explain the development of Coastal Video Monitoring System (CoViMoS) with the main characteristics including low-cost and easy implementation. CoViMoS characteristics have been realized using the device IP camera for video image acquisition, and development of software applications with the main features including detection of shoreline and it changes are automatically. This capability was based on segmentation and classification techniques based on data mining. Detection of shoreline is done by segmenting a video image of the beach, to get a cluster of objects, namely land, sea and sky, using Self Organizing Map (SOM) algorithms. The mechanism of classification is done using K-Nearest Neighbor (K-NN) algorithms to provide the class labels to objects that have been generated on the segmentation process. Furthermore, the classification of land used as a reference object in the detection of costline. Implementation CoViMoS system for monitoring systems in Cucukan Beach, Gianyar regency, have shown that the developed system is able to detect the shoreline and its changes automatically.

SIMS: a Store Image Monitoring System

1984 ◽  
Vol 3 (2) ◽  
pp. 129-138 ◽  
Author(s):  
Graham J. Hooley ◽  
David E. Cook
Keyword(s):  
Monitoring System ◽  
Store Image ◽  
Image Monitoring

Research of Bus Video Monitoring System Based on WCDMA

2014 ◽  
Vol 543-547 ◽  
pp. 891-894
Author(s):  
Lian Jun Zhang ◽  
Shi Jie Liu
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Transmission System ◽  
Video Data ◽  
Video Monitoring ◽  
Video Server ◽  
Real Time Transmission ◽  
Monitoring Center ◽  
Server System ◽  
Transmission Ability

The bus video monitoring system is composed by WCDMA transmission system, video server system, system monitoring center and outreach system. By WCDMA wireless transmission module achieving real time video data return, while using VPDN network technology. Using of the DVS video server and by WCDMA transmission system, the monitoring videos information will be transmitted to the monitoring center rapidly and in real time. The monitoring center can remotely monitor, manage, and dispatch the bus. The results demonstrating this system has good real time transmission ability.

scholarly journals A Multiobjective Piglet Image Segmentation Method Based on an Improved Noninteractive GrabCut Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Feilong Kang ◽  
Chunguang Wang ◽  
Jia Li ◽  
Zheying Zong
Keyword(s):  
Image Segmentation ◽  
Real Time ◽  
Monitoring System ◽  
Structural Similarity ◽  
Morphological Processing ◽  
Video Monitoring ◽  
Segmentation Method ◽  
Time Requirements ◽  
And Behavior ◽  
Grabcut Algorithm

In the video monitoring of piglets in pig farms, study of the precise segmentation of foreground objects is the work of advanced research on target tracking and behavior recognition. In view of the noninteractive and real-time requirements of such a video monitoring system, this paper proposes a method of image segmentation based on an improved noninteractive GrabCut algorithm. The functions of preserving edges and noise reduction are realized through bilateral filtering. An adaptive threshold segmentation method is used to calculate the local threshold and to complete the extraction of the foreground target. The image is simplified by morphological processing; the background interference pixels, such as details in the grille and wall, are filtered, and the foreground target marker matrix is established. The GrabCut algorithm is used to split the pixels of multiple foreground objects. By comparing the segmentation results of various algorithms, the results show that the segmentation algorithm proposed in this paper is efficient and accurate, and the mean range of structural similarity is [0.88, 1]. The average processing time is 1606 ms, and this method satisfies the real-time requirement of an agricultural video monitoring system. Feature vectors such as edges and central moments are calculated and the database is well established for feature extraction and behavior identification. This method provides reliable foreground segmentation data for the intelligent early warning of a video monitoring system.

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