Even with all the effort made to date, background subtraction, which is applicable to still camera images, continues to face a number of difficulties. The principle behind background subtraction is to subtract and threshold a background model image from the current inhibitor manufacture frame. The result gives the differences between the two subtracted images, and it is hypothesized Inhibitors,Modulators,Libraries that these differences correspond to moving objects. In practice, this is not always the case, as differences may correspond to shadows, changes Inhibitors,Modulators,Libraries of lighting, or camera noise. Furthermore, some of them may correspond to changes in an image, like waving leaves or waves on a lake, which are irrelevant to the application. The challenge, then, is to propose a background model that allows filtering of these unavoidable perturbations, while still correctly detecting the moving objects of interest.

Many background subtraction methods have been proposed with different models and update strategies. Most rely on the difference between individual Inhibitors,Modulators,Libraries pixels. Since perturbations often affect individual pixels, this may cause misdetection when performing differentiation, as observed in [1]. Our hypothesis is that using a neighborhood around a pixel should allow the filtering of perturbations that affect only a few pixels in a region. We propose a method where background subtraction is performed iteratively from large to small rectangular regions using color histograms and a texture measure. In addition, the classical Gaussian Mixture method [2] is used at the smallest region level to improve the results even more.

In order to give Inhibitors,Modulators,Libraries more accurate distance measures, which account for the error distribution among the histogram bins [3], the Minimum Difference of Pair Assignments (MDPA) distance is applied on the histograms. GSK-3 This algorithm and its analysis constitute the contribution of this paper.We thoroughly tested our method by comparing detected moving regions with ground-truth regions using true and false positive rate measures. We also characterized the impact of parameter change on the results to evaluate parameter sensitivity and stability. The results show that our proposed method, combined with Gaussian Mixture, outperforms Gaussian Mixture alone and other state-of-the-art methods.One of the advantages of our proposed approach compared to state-of-the-art methods is that it reduces the number of false detections, as pixel-level differentiation can be performed in regions with significant motion only.

Another advantage is that the subdivision of large regions MEK162 into small ones can be stopped before pixel level is reached. So, if required, only a coarse background subtraction need be performed (see Figure 1).Figure 1.Motion detection at different scales. Finest rectangle size of. (a) 4 �� 3; (b) 16 �� 12; and (c) 32 �� 24.The paper is structured as follows. Section 2.