Hysteresisbased selective gaussianmixture model for realtime background update We propose a novel Mixture of Gaussian (MOG)based realtime background update technique. The proposed technique consists of a new selective matching scheme based on the combined approaches of component ordering and winnertakesall. This matching scheme not only selects the most probable component for the first matching with new pixel data, greatly improving performance, but also simplifies pixel classification and component replacement in case of no match. Further performance improvement achieved by using a new simple and functional component variance adaptation formula. Also in this technique, the proposed new hysteresisbased component matching and temporal motion history schemes improve segmentation quality. Component hysteresis matching improves detected foreground object blobs by reducing the amount of cracks and added shadows, while motion history preserves the integrity of moving objects boundaries, both with minimum computational overhead. The proposed background update technique implicitly handles both gradual illumination change and temporal clutter problems. The problem of shadows and ghosts is partially addressed by the proposed hysteresisbased matching scheme. The problem of persistent sudden illumination changes and camera movements are addressed at frame level depending on the percentage of pixels classified as foreground. We implemented three different stateoftheart background update techniques and compared their segmentation quality and computational performance with those of the proposed technique. Experimental results on reference outdoor sequences and real traffic surveillance streams show that the proposed technique improved segmentation accuracy for extracting moving objects of interest compared to other reference techniques.

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.