A Comparative Study of Background Estimation Algorithms

ABSTRACT: Also the work entailed an effective shadow removal technique which is used to avoid detection of shadow pixels as part of the foreground mask. The results show some critical tradeoffs between precision and speed of the process. For instance, although approximated median filtering seems to be a suitable approach due to its simplicity in computation, it fails to detect foreground objects accurately when the background scene contains movements, in addition it is slow in the case of adapting to frame changes which makes this algorithm impractical for many outdoor applications. The results of progressive method indicate that the algorithm is able to handle the adaptation or deal more effectively than approximated median filtering with even better accuracy for foreground extracting in expense of slightly losing the performance speed. However, the background movement problem (shaking leaves, flag in the wind, flickering, etc) still stands. Mixture of Gaussians based results was promising in both adaptation and precision however the method’s sensitivity to transient stops and its heavier computational complexity were its main drawbacks. Finally although the group based histogram was still too sensitive to fluctuation of light it led to acceptable results introducing itself as a reliable background-foreground segmentation method for its ability to deal with transient stops. Keywords: Temporal Median Filtering,Background estimation,Mixture of Gaussians background estimation, Median filtering, Histogram, Precision and recall, Shadow removal. ……………………………………………………………………………………………………………………………………………………………………………………………………………………