III WVC 2007 - Iris.sel.eesc.sc.usp.br - USP

WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.Background Estimation on Motion Scenes Using K-MeansBruno Alberto MedeirosUniversidade de São PauloEscola de Engenharia de São CarlosDepartamento de Engenharia Elétricabam@barretos.com.brLuiz Marcelo Chiesse da SilvaUniversidade Tecnológica Federal do ParanáCampus Cornélio ProcópioDepartamento de Eletrotécnicachiesse@utfpr.edu.brAdilson GonzagaUniversidade de São PauloEscola de Engenharia de São CarlosDepartamento de Engenharia Elétricaadilson@sel.eesc.usp.brAbstractBackground subtraction techniques are widely used invideo systems for internal or external surveillance andvehicle traffic tracking, by motion detection and imagesegmentation. These techniques generally differentiateand separate the motion pixels from pixels in a still area,identifying the foreground and the background in a videoscene. For purpose of computational efficiency inembedded systems, it can be done recursively averagingthe frames, and estimating the background from thisaverage. In a certain level of complexity, there are somedetails to leave in count, like soft changes in backgroundpixels, that must be rejected, or a motion background.The main aspect to consider is the fast convergence of achanging pixel (motion pixel) to the average (backgroundestimated pixel). This paper is based in the background estimation algorithm, increasing theconvergence of the estimation using the k-meansalgorithm to reach the background.1. IntroductionBackground subtraction methods are applied mainly intraffic and security systems and motion detection insurveillance systems, by the segmentation of movingareas in the frames. Generally is needed an embeddedsystem, and for this purpose, the system must be real-timeand use few computational power and memory. TheGaussian mixtures [1],[2] and supervised statisticalmachine learning techniques [3] generally claims forcomputation and memory efficiency. For these features,the background estimation algorithm [4] is a betterchoice. The aim here is modify the algorithm for abetter performance, using the k-means algorithm. Unlikeclustering based in probability models for segmentation[5] or clustering segmentation techniques [6], here the k-means algorithm is used to auxiliate the algorithm.2. background estimationThis method begins with the estimation of thebackground calculating the mean at each frame in thevideo sequence. In the conventional algorithm, eachpixel level increments or decrements the backgroundmean, updating the estimation (if there are no change, themean value doesn’t change). The variance is calculatedtoo by the same process with the non-zero differences andis given to each pixel a label (motion or stationary pixel),in a recursively framework. In figure 1, M is thebackground mean, I the actual frame, V the variance andD the pixel label. N is the number of non-zero differencesfor a pixel between frames. According with [4], the rangevalue of N is small (between 1 and 4), and usually is apower of 2. In the initialization, if the pixel is not locatedin a background area, it could claims a great number offrames to converge to the mean, producing the ghosteffect due to the slow convergence to the mean. background estimation with k-meansHere, the pixel level is splited in clusters replacing theincrement/decrement in the algorithm by the clusternumber given by the k-means algorithm, in each pixel.The pixel level is clusterized in each frame processing,351