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

Authors(1) :-

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.

Authors and Affiliations



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

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Publication Details

Published in : Volume 2 | Issue 7 | September 2017
Date of Publication : 2017-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 280-287
Manuscript Number : CSEIT174433
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

, "A Comparative Study of Statistical and Machine Learning Techniques of Background Subtraction in Visual Surveillance", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.280-287, September-2017.
Journal URL : http://ijsrcseit.com/CSEIT174433

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