Abstract
Dynamic background estimation (DBE) and background subtraction (DBS) is a very effective way for moving object tracking when (a part of) background is continually fluctuating. In DBE, newest time-series video frames are kept and the pixel values at the same position in those frames are used to determine the value of background pixels. We propose an accurate and fast DBE/DBS method by defining a chromaticity-based color space which is less affected by brightness change, and by defining a similarity metric based on the distance in the color space. In our DBE, the pixel with the highest average similarity is determined as the background. By utilizing the statistical value derived from the calculation of the average similarities, the threshold value for our DBS after our DBE is adaptively determined. In the evaluation of Precision-Recall relation and F-measure of foreground extraction with public data set, our method with a median filter as pre-process showed comparable or better performance than other representative methods. The processing speed for VGA images was 24 fps that is much faster than other methods. Our method with resizing before a median filter showed about 10 point higher F-measure than others and 100 fps of the speed so that it is applicable to real-time object tracing systems.
