AbstractsComputer Science

Detecting moving objects plays a crucial role in an intelligent surveillance system. Object detection is often integrated with various tasks, such as tracking objects, recognizing their behaviours and alerting when abnormal events occur. However, it suffers from non-stationary background in surveillance scenes, especially in two potentially dynamic cases: (1)sudden illumination variation, such as outdoor sunlight changes and indoor lights turning on/off; (2)burst physical motion, such as the motion of indoor artificial background, which include fans, escalators and auto-doors, and the motion of natural background, which include fountain, ripple on water surface and swaying tree. If the actual background includes a combination of any of these factors, it becomes much more difficult to detect objects. Traditional algorithms,i.e.Gaussian Mixture Model(GMM) and Kernel Density Estimation (KDE) handle gradual illumination changes by building the statistical background models progressively using long-term leaning frames. In practice, however, this kind of independent pixel-wise model often fail to avoid mistakenly integrating foreground elements into the background, and it is difficult to adapt to sudden illumination change and burst motion. On the other hand, spatial-dependence model, i.e. Grayscale Arranging Pairs(GAP) and Statistical Reach Feature(SRF), shows promising performance under illumination change and other dynamic background. This study proposes a novel framework to build a background model for object detection, which is evolved from GAP method and SRF method. It is brightness-invariant and able to tolerate burst motion. We name it Co-ocurrence Probability based Pixel Pairs (CP3). In order to model the dynamic background, spatial pixel pairs with high temporal Co-ocurrence probability are employed to represent each other by using the stable intensity differential increment between a pixel pair which is much more reliable than the intensity of a single pixel, especially when the intensity of a single pixel changes dramatically over time. The model performs robust detection under outdoor and indoor extreme environments. Compared with the independent pixel-wise background modelling methods, CP3 determines stable Co-ocurrence pixel pairs, instead of building the parameterized/non-parametrized model for a single pixel. These pixel pairs maintain a reliable background model, which can be used to capture structural background motion and cope with local and global illumination changes. As a spatial-dependence method, CP3 does not predefine/assume any local operator, subspace or block for an observed pixel, but it does its best effort to select those qualified supporting pixels which could maintain reliable linear relationship with the target pixels. Moreover, based on the single Gaussian model of the differential value of the pixel pair, it provides an accurate detection criterion even the gray-scale dynamic range is compressed under weak illumination. The proposed method can be used for modelling the appearance of…