A Comparative Study of Statistical and Machine Learning Techniques of Background Subtraction in Visual Surveillance

Authors

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Keywords:

Meanshift, Kalman Filter, Particle Filter, Motion Segmentation, Background Subtraction.

Abstract

Video is basically collection of images. Through a single image we can take a screenshot of a scene, which helps in detecting motion with sequence. Now a days, video has popular usage in many applications like identification of exceptional behavior in parking, monitoring of traffic, finding the cause of road accidents, detection of pedestrians, ATMs etc. This is done with the help of many applications that include object tracking, motion segmentation using one of its part background subtractions with the help of various algorithms such as particle filter, mean shift method, kalman filter etc. This paper presents a survey on various algorithms that helps in improving the motion of the object. Research is made on motion detection and tracking in videos along with comparative analysis on various algorithms.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-09-30

Issue

Volume 2, Issue 7, September-2017

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
, " A Comparative Study of Statistical and Machine Learning Techniques of Background Subtraction in Visual Surveillance, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.280-287, September-2017.