In this paper, we introduce a Bayesian approach on multiple people localization in multicamera systems. First, pixel-level features are extracted, which are based on physical properties of the 2-D image formation process, and provide information about the head and leg positions of the pedestrians, distinguishing standing and walking people, respectively. Then, features from the multiple camera views are fused to create evidence for the location and height of people in the ground plane. This evidence accurately estimates the leg position even if either the area of interest is only a part of the scene or the overlap ratio of the silhouettes from irrelevant outside motions with the monitored area is significant. Using this information, we create a 3-D object configuration model in the real world. We also utilize a prior geometrical constraint, which describes the possible interactions between two pedestrians. To approximate the position of the people, we use a population of 3-D cylinder objects, which is realized by a marked point process. The final configuration results are obtained by an iterative stochastic energy optimization algorithm. The proposed approach is evaluated on two publicly available datasets, and compared to a recent state-of-the-art technique. To obtain relevant quantitative test results, a 3-D ground truth annotation of the real pedestrian locations is prepared, while two different error metrics and various parameter settings are proposed and evaluated showing the advantages of our proposed model.

In this paper we introduce a probabilistic approach on multiple person localization using multiple calibrated camera views. People present in the scene are approximated by a population of cylinder objects in the 3-D world coordinate system, which is a realization of a Marked Point Process. The observation model is based on the projection of the pixels of the obtained motion masks in the different camera images to the ground plane and to other parallel planes with different height. The proposed pixel-level feature is based on physical properties of the 2-D image formation process and can accurately localize the leg position on the ground plane and estimate the height of the people, even if the area of interest is only a part of the scene, meanwhile silhouettes from irrelevant outside motions may significantly overlap with the monitored region in some of the camera views. We introduce an energy function, which contains a data term calculated from the extracted features and a geometrical constraint term modeling the distance between two people. The final configuration results (location and height) are obtained by an iterative stochastic energy optimization process, called the Multiple Birth and Death dynamics. The proposed approached is compared to a recent state-of-the-art technique in a publicly available dataset and its advantages are quantitatively demonstrated.

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.

In this paper we present robust statistical methods for segmentation and preprocessing of time-multiplexed videos. We will present hidden Markov and hidden semi-Markov model based real-time detectors and their usage for video segmentation and anomalous event detection. We demonstrate the high performance of our detectors with real-life outdoor videos from low-quality cameras.

We propose novel pixel dense modeling of motion of urban traffic in noisy environments with the help of multidimensional Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs). In our approach there is no need for object tracking in order to detect anomalous motion or to model and visualize the fluctuation of traffic. We propose a new scaling method introduced into the HMM to get a robust tool for the analysis of hundreds of motion vector samples at a time. We show the use of our model with a photorealistic video synthetized from real life recordings.