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We proposed a novel approach to track groups of people observed by a static camera. The approach relies on a unified Bayes' theorem based framework to model both scene background and the color distribution of targets. By sharing the framework, it is more simple and low cost to construct a practical surveillance system. Additionally, the framework has the advantages of insensitiveness to initial observations...
Camera networks are important in a video surveillance system. Many surveillance systems research has been done; most of it utilizes a general purpose computer. However, the latest trend is to use small, task specific and low power computer to process and control the system; in another word using embedded systems. This paper presents a camera network localization algorithm to be implemented on a FPGA...
Detecting moving objects is an important part of tracking. Most of the previous work on moving object detection concentrates on fixed cameras. Methods using moving cameras seldom deal with the problem of robustly and continuously updating the background model during all times including the periods when the camera is not static. We propose a method to build and continuously update a background model,...
This paper describes an algorithm for visible/infrared fusion for video surveillance. Our technique combines signal-level and decision-level multispectral information fusion. We combine several techniques: we model observations in each spectral channel by a typical pixel-level mixture-of-Gaussian-based model; we model high level factors such as confidence of each modality, motion, object area, and...
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