Improved Gaussian Mixture Model for the Task of Object Tracking This paper presents an object tracking system. Our goal is to create a realtime object tracker that can handle occlusions, track multiple objects that are rigid or deformable, and on indoor or outdoor sequences. This system is composed of two main modules: motion detection and object tracking. Motion detection is achieved using an improved Gaussian mixture model. Based on multiple hypothesis of object appearance, tracking is achieved on various levels. The core of this module uses regions local and global information to match these regions over the frame sequence. Then higher level instances are used to handle uncertainty, such as missmatches, objects disappearance, and occlusions. Finally, merges and splits are detected for further occlusions detection.

Nonlinear prediction for Gaussian mixture image models Prediction is an essential operation in many image processing applications, such as object detection and image and video compression. When the image is modeled as Gaussian, the optimal predictor is linear and easy to obtain. However, image texture and clutter are often nonGaussian and in such cases, optimal predictors are difficult to find. In this paper, we have derived an optimal predictor for an important class of nonGaussian image models, the blockbased multivariate Gaussian mixture model. This predictor has a special nonlinear structure: it is a linear combination of the neighboring pixels but the combination coefficients are also functions of the neighboring pixels, not constants. The efficacy of this predictor is demonstrated in object recognition experiments where the prediction error image is used to identify hidden objects. Results indicate that when the background texture is nonlinear, i.e., with fastswitching graylevel patches, it performs significantly better than the optimal linear predictor.