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Call for Papers - March 2017 Edition

International Journal of Computer Applications Technology and Research (IJCATR) call for research paper for Volume 6 Issue 3 March 2017 Edition. Submit manuscript to editor@ijcat.com. Last date of manuscript submission is February 28, 2017.

 

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International Journal of Computer Applications Technology and Research (IJCATR)

Volume 3 Issue 5 May 2014

Moving Object Detection with Fixed Camera and Moving Camera for Automated Video Analysis

Dipali Shahare, Ranjana Shende

10.7753/IJCATR0305.1001

    
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Keywords: Object Detection, Soft Impute method, Markov Random Field, Temporal Differencing, Moving object extraction, background subtraction.

Abstract References BibText Highlights

        Detection of moving objects in a video sequence is a difficult task and robust moving object detection in video frames for video surveillance applications is a challenging problem. Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Frequently, an object detector requires manual labeling, while background subtraction needs a training sequence. To automate the analysis, object detection without a separate training phase becomes a critical task. This paper presents a survey of various techniques related to moving object detection and discussed the optimization process that can lead to improved object detection and the speed of formulating the low rank model for detected object.

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@article{dipali03051001,
title = "Moving Object Detection with Fixed Camera and Moving Camera for Automated Video Analysis ",
journal = "International Journal of Computer Applications Technology and Research",
volume = "3",
number = "5 ",
pages = "277 - 283",
year = "2014",
author = "Dipali Shahare, Ranjana Shende ",
}

The paper proposes an approach in which integrates object detection and background subtraction into a single process of optimization for speedup the computation.
To improve the accuracy detecting the object in video and cut down the cost of computations using the process of optimization.
The objective is to handle static and dynamic background while process the video.
In this paper, later will work on outliers present in video, in this it detects and removes outlier (i.e. noise) present in sequence of frames.