A spatially distributed model for foreground segmentation

Foreground segmentation is a fundamental first processing stage for vision systems which monitor real-world activity. In this paper we consider the problem of achieving robust segmentation in scenes where the appearance of the background varies unpredictably over time. Variations may be caused by processes such as moving water, or foliage moved by wind, and typically degrade the performance of standard per-pixel background models. Our proposed approach addresses this problem by modeling homogeneous regions of scene pixels as an adaptive mixture of Gaussians in color and space. Model components are used to represent both the scene background and moving foreground objects. Newly observed pixel values are probabilistically classified, such that the spatial variance of the model components supports correct classification even when the background appearance is significantly distorted. We evaluate our method over several challenging video sequences, and compare our results with both per-pixel and Markov Random Field based models. Our results show the effectiveness of our approach in reducing incorrect classifications

Target Tracking in Confined Environments with Uncertain Sensor Positions

To ensure safety in confined environments such as mines or subway tunnels, a (wireless) sensor network can be deployed to monitor various environmental conditions. One of its most important applications is to track personnel, mobile equipment and vehicles. However, the state-of-the-art algorithms assume that the positions of the sensors are perfectly known, which is not necessarily true due to imprecise placement and/or dropping of sensors. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions and target tracking. We divide the considered area in a finite number of cells, define dynamic and measurement models, and apply a discrete variant of belief propagation which can efficiently solve this high-dimensional problem, and handle all non-Gaussian uncertainties expected in this kind of environments. Finally, we use ray-tracing simulation to generate an artificial mine-like environment and generate synthetic measurement data. According to our extensive simulation study, the proposed approach performs significantly better than standard Bayesian target tracking and localization algorithms, and provides robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201

Color models of shadow detection in video scenes

In this paper we address the problem of appropriate modelling of shadows in color images. While previous works compared the different approaches regarding their model structure, a comparative study of color models has still missed. This paper attacks a continuous need for defining the appropriate color space for this main surveillance problem. We introduce a statistical and parametric shadow model-framework, which can work with different color spaces, and perform a detailed comparision with it. We show experimental results regarding the following questions: (1) What is the gain of using color images instead of grayscale ones? (2) What is the gain of using uncorrelated spaces instead of the standard RGB? (3) Chrominance (illumination invariant), luminance, or ”mixed” spaces are more effective? (4) In which scenes are the differences significant? We qualified the metrics both in color based clustering of the individual pixels and in the case of Bayesian foreground-background-shadow segmentation. Experimental results on real-life videos show that CIE L*u*v* color space is the most efficient

Video foreground detection based on symmetric alpha-stable mixture models.

Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. These assumptions restrict the applicability of BS methods to real-time object detection in video. In this paper, we propose an extended cluster BS technique with a mixture of symmetric alpha stable (SS) distributions. An on-line self-adaptive mechanism is presented that allows automated estimation of the model parameters using the log moment method. Results over real video sequences from indoor and outdoor environments, with data from static and moving video cameras are presented. The SS mixture model is shown to improve the detection performance compared with a cluster BS method using a Gaussian mixture model and the method of Li et al. [11]

The Design and Implementation of Human Activity and Crowd Behavior Analysis System in Video Surveillance

近年来，视频监控系统广泛应用在人们日常生活的多个领域中，如企业与居家安防、刑侦、银行监控、医院和交通流量监控等。但是，现有的监控系统只是事后取证的手段，缺乏智能化的事件发现和报警。人体行为和人群行为监控是视频监控的核心，智能化行为判定能够为人们提供及时可靠的实时信息，对异常事件做出快速反应。因而，视频监控中人体人群行为分析具有重要的现实意义。本文主要完成视频监控中人体行为和人群行为的分析，主要工作如下： (1)人体行为主要针对于人体运动行为，运动行为获取运用了前景检测和目标跟踪算法。在深入研究了VIBE算法和帧间差分算法之后，本文提出了改进后的前景检测算法。在此基础上，使用直方图匹配优化的压...In recent years, video surveillance system are used in the areas of people's daily life, such as enterprises and home security, criminal investigation, monitoring banks, hospitals and traffic monitoring. However, the existing monitoring system is only using to obtain evidence after the accident, lacking intelligent event detection and alarm. Human activity and crowd monitoring is the core of video...学位：工程硕士院系专业：信息科学与技术学院_工程硕士(计算机技术)学号：2302013115320