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This article proposes a Robust E-hog for Feature Analysis (REFA) to describe interest points and their neighborhood. Initially the two most used methods: SIFT and SURF are studied and various advantages (invariances, repeatability) are extracted to create a new approach (detection, description and matching). First, the Fast-Hessian detector is used because it gives the best repeatability rate, however it will be optimized. Secondly the local neighborhood description is based on a histogram of oriented gradients on an elliptical shape. Finally a decision tree, validation threshold and deletion duplicates are used to match interest points. This method must also be as robust as possible for image transformations (rotations, scales, viewpoints for example). All tool parameters (orientations, thresholds, analysis shape) will be also detailed in this article.

Pan Tilt Zoom cameras have the ability to cover wide areas with an adapted resolution. Since the logical downside of high resolution is a limited field of view, a guard tour can be used to monitor a large scene of interest. However, this greatly increases the duration between frames associated to a specific location. This constraint makes most background algorithms ineffective. In this article we propose a background subtraction algorithm suitable to cameras with very low frame rate. Its main interest consists in the resulting robustness to sudden illumination changes. The background model which describes a wide scene of interest consisting of a collection of images can thus be successfully maintained. This algorithm is compared with the state of the art and a discussion regarding its properties follows.

Video surveillance becomes more and more extended in industry and often involves automatic calibration system to remain efficient. In this paper, a video-surveillance system that uses stationary-dynamic cameras devices is presented. The static camera is used to monitor a global scene. When it detects a moving object, the Pan-Tilt-Zoom (PTZ) camera is controlled to be centered on this object. We describe a method of camera-to-camera calibration, integrating zoom calibration in order to command the angles and the zoom of the PTZ camera. This method enables to take into account the intrinsic camera parameters, the 3D scene geometry and the fact that the mechanism of inexpensive camera does not fit the classical geometrical model. Finally, some experiment results attest the accuracy of the proposed solution.