A background subtraction method using color information in the frame averaging process

Accurate and reliable detection of moving object from a video sequence is often hard to do in real case. The most common approach is image segmentation which identifies the object from the video sequence that differs significantly from a background model. Among the other segmentation methods background subtraction is the effective and efficient one. A background subtraction method must gratify some challenges, first of all the method must diminish the noise from the sensor that results alteration in pixel color in the probable background from the original in the current video sequence. Secondly the method must automatically fiddle with the changing scene conditions. All these challenges must satisfy in real time with lower computational complexity. In this paper we represent a background subtraction technique that learns the variation of each pixel throughout the video in terms of average and average differences of pixels color of current frame with the previous one. The BGR (Blue, Green and Red) image is first converted to HSV (Hue, Saturation and Value) color space. And then the algorithm is applied to the H plane only as H plane is invariant to illumination changes. Morphological operations are done for removing image noises. In the last stage edges are detected to find out whether there is any false detection or not. Our experimental result shows that this method is very much reliable in changing scene conditions and noise elimination.