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Background modeling and subtraction is a classical topic in compute vision. Gaussian mixture modeling (GMM) is a popular choice for its capability of adaptation to background variations. Lots of improvements have been made to enhance the robustness by considering spatial consistency and temporal correlation. In this paper, we propose a sharable GMM based background subtraction approach. Firstly, a...
Behavior analysis across multi-cameras becomes more and more popular with the rapid development of camera network in video surveillance. In this paper, we propose a novel unsupervised graph matching framework to associate trajectories across partially overlapping cameras. Firstly, trajectory extraction is based on object extraction and tracking and is followed by a homographic projection to a mosaic-plane...
The main issue of video copy detection is to estimate a constant spatial-temporal transformation in object level between the original video and the copies. In this paper, we propose a multi-level trajectory modeling approach for video copy detection. It includes a rich trajectory description and a robust trajectory-to-trajectory matching to preserve and explore the trajectory characteristics in both...
Recently, the covariance region descriptor has been proved robust and versatile for a modest computational cost. It enables efficient fusion of different types of features. Based on the covariance descriptor and the metric on Riemannian manifolds, we develop a robust Bayesian tracking framework via fragments-based representation in this paper. In this framework, the template object is represented...
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