Call For Paper Volume:4 Issue:8 Aug'2017 |

Self-Balanced SENsitivity SEgmenter change to static detection Method With Local Adaptive Sensitivity

Publication Date : 30/09/2015



Author(s) :

Swathy Mani , Riyamol Sidhic.


Volume/Issue :
Volume 2
,
Issue 9
(09 - 2015)



Abstract :

Foreground/background segmentation via change detection in video sequences is often used as a stepping stone in high-level analytics and applications. There are many methods are developed for this problem. In this paper propose a novel approach for detecting the change or moving vehicles as well as the suddenly stopping vehicles. These are used in more traffic surveillance applications. This approach uses spatiotemporal binary features as well as color information to detect changes. we use pixel-level feedback loops to dynamically adjust our method’s internal parameters without user intervention. This paper is generally focused on the detection of moving object and static object. Moving object detection is the finding of foreground objects and static object detection is the background objects. This also helps the authority to detect parking vehicles in no parking area . The use of change detection algorithms to identify regions of interest in video sequences has long been a stepping stone in high level surveillance applications.


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Self-Balanced SENsitivity SEgmenter change to static detection Method With Local Adaptive Sensitivity

September 29, 2015