The aim of the paper is to show that localization of observed moving objects is possible in a multicamera environment, based on the simple foreground mask and the estimation of the size distribution on image plane. The method is flexible, it can handle arbitrary number of cameras and objects. It is based only on change detection masks and does not depend on appearance information. Due to its statistical nature the proposed method efficiently handles such challenging situations as changes in viewpoint, occlusions due to view variations, background clutter.

The paper introduces a video surveillance and event detection framework and application for semi-supervised surveillance use. The systempsilas intended use is in automatic mode on camera feeds that are not actively watched by surveillance personnel, and should raise alarms when unusual events occur. We present the current detector filters, and the extendable modular interface. Filters include local and global unusual motion detectors, left/stolen object detector, motion detector, tampering/failure detector, etc. The system stores the events and associated data, which can be organized, searched, annotated and (re)viewed. It has been tested in real life situation for police street surveillance.

Extraction of foreground is a basic task in surveillance video analysis. In most real cases, its performance is heavily based on the efficiency of shadow detection and on the analysis of lighting conditions and reflections caused by mirrors or other reflective surfaces. This correspondence is focused on the improvement of foreground extraction in the case of planar reflective surfaces. We show that the geometric model of a scene with a planar reflective surface is reduced to the estimation of vanishing-point for the case of an auto-epipolar (skew-symmetric) fundamental matrix. The correspondences for the vanishing-point estimation are extracted from motion statistics. The knowledge of the position of the vanishing point allows us to integrate the geometric model and the motion statistics into image foreground-extraction to separate foreground from reflections, and thus to achieve better performance. The experiments confirm the accuracy of the vanishing point and the improvement of the foreground image mask by removing reflected object parts.

This paper presents a robust walk-detection algorithm, based on our symmetry approach which can be used to extract gait characteristics from video-image sequences. To obtain a useful descriptor of a walking person, we temporally track the symmetries of a person's legs. Our method is suitable for use in indoor or outdoor surveillance scenes. Determining the leading leg of the walking subject is important, and the presented method can identify this from two successive walk steps (one walk cycle). We tested the accuracy of the presented walk-detection method in a possible application: Image registration methods are presented which are applicable to multicamera systems viewing human subjects in motion.

A new motion-based method is presented for automatic registration of images in multicamera systems, to permit synthesis of wide-baseline composite views. Unlike existing static-image and motion-based methods, our approach does not need any a priori information about the scene, the appearance of objects in the scene, or their motion. We introduce an entropy-based preselection of motion histories and an iterative Bayesian assignment of corresponding image areas. Finally, correlated point-histories and data-set optimization lead to the matching of the different views. The method is validated by demonstrating its successful use on several real-life indoor and outdoor stereo video image-sequence pairs.

In this paper we address the problem of estimating the position of the vanishing point in an outdoor camera-mirror environment, making use of motion statistics derived from a video sequence. Knowledge of the vanishing-point position is the key for the geometrical modeling of reflective surfaces or cast shadows; this allows the (future) integration of the model into foreground-extraction methods to achieve better performance. Thanks to the use of co-motion statistics in the correspondence-detection step, our approach gives robust results. The presented optimization method for final parameter estimation utilizes motion statistics to determine an improved fitting function in case of wide-baseline views and in cases where the correspondences are corrupted with considerable amounts of noise. Results are presented for several video sequences representing a variety of scene layouts and object types. The outcomes show that our approach gives robust results in the context of widely different environments.

We outline our latest methods and algorithmic solutions for detection of concurrent motions and registering cameras in real-life surveillance systems. We show that cameras can be registered in several rather unfavorable conditions, based on: (1) unpredictable motion without structured background or defined object shapes or (2) walking persons of undefined silhouettes and short detectable walking distances or (3) shadows of undefined structures in front of flickering background. These methods are tested in real-life sequences and we show that flexible 3D camera registration is possible even in bad lighting conditions and in the lack of any known structures or motions.

In this paper we present a symmetry-based approach which can be used to detect humans and to extract biometric characteristics from video image-sequences. The method employs a simplified symmetry-feature extracted from the images. To obtain a useful descriptor of a walking person, we track temporally the symmetries which result from the movements of the person's legs. In a further processing stage these patterns are filtered, then re-sampled using Bezier-splines to generate an invariant and noise-cleaned signature or "feature". In our detection method the extracted spatio-temporal feature with a large number of dimensions (800) is transformed to a space with a much smaller number of dimensions (3), which we call the "eigenwalks space"; the method uses principal component analysis (PCA) to reduce the dimensionality, and the support vector machine (SVM) method in the eigenspace for recognition purposes. Finally we present a method by which we can estimate the gait-parameters (the beginning and end of a walk-cycle, identification of the leading leg) from the symmetry-patterns of the walking person, without camera calibration, based on two successive detected walk-steps.

In the paper we introduce an algorithm for matching partially overlapping image-pairs where the object of interest is in motion, even if the motion is discontinuous and in an unstructured environment. In our previous work [Z. Szlavik et al, 2004] we have shown that by using co-motion statistics matching of overlapping views can be done and then the projective geometry can be estimated. Here, we show how to optimize searching for concurrently moving pixels. The robust algorithm we describe here finds point correspondences in two images by using entropy-based thresholding and without searching for any structures and without the need for tracking continuous motion. Our method makes it possible to (re)calibrate multicamera systems without human assistance.