Improved Background Matching Framework for Motion Detection
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - August Issue 2013 by IJCTT Journal|
|Volume-4 Issue-8 |
|Year of Publication : 2013|
|Authors :K.Amaleswarao, G.Vijayadeep,U.Shivaji|
K.Amaleswarao, G.Vijayadeep,U.Shivaji "Improved Background Matching Framework for Motion Detection"International Journal of Computer Trends and Technology (IJCTT),V4(8):2873-2877 August Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract:- Real-time detection of moving objects is vital for video surveillance. Background subtraction serves as a basic method typically used to segment the moving objects in image sequences taken from a camera. Some existing algorithms cannot fine-tune changing circumstances and they need manual calibration in relation to specification of parameters or some hypotheses for dynamic changing background. An adaptive motion segmentation and detection strategy is developed by using motion variation and chromatic characteristics, which eliminates undesired corruption of the background model and it doesn`t look on the adaptation coefficient. In this particular proposed work, a novel real-time motion detection algorithm is proposed for dynamic changing background features. The algorithm integrates the temporal differencing along with optical flow method, double background filtering method and morphological processing techniques to achieve better detection performance. Temporal differencing is designed to detect initial motion areas for the optical-flow calculation to produce real-time and accurate object motion vectors detection. The double background filtering method is obtain and keep a reliable background image to handle variations on environmental changing conditions that is designed to get rid of the background interference and separate the moving objects from it. The morphological processing methods are adopted and mixed with the double background filtering to obtain improved results. The most attractive benefit for this algorithm is that the algorithm does not require to figure out the background model from hundreds of images and can handle quick image variations without prior understanding of the object size and shape.
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Keywords : — Background Model, Optical Flow model ,Image Segmentation, Background detection.