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Tracking across cameras with non-overlapping views is a challenging problem. Firstly, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in(More)
Conventional tracking approaches assume proximity in space, time and appearance of objects in successive observations. However, observations of objects are often widely separated in time and space when viewed from multiple non-overlapping cameras. To address this problem, we present a novel approach for establishing object correspondence across(More)
When viewed from a system of multiple cameras with non-overlapping fields of view, the appearance of an object in one camera view is usually very different from its appearance in another camera view due to the differences in illumination , pose and camera parameters. In order to handle the change in observed colors of an object as it moves from one camera(More)
We present a background subtraction method that uses multiple cues to robustly detect objects in adverse conditions. The algorithm consists of three distinct levels i.e pixel level, region level and frame level. At the pixel level, statistical models of gradients and color are separately used to classify each pixel as belonging to background or foreground.(More)
In this paper, we propose a robust approach for tracking targets in forward looking infrared (FLIR) imagery taken from an airborne moving platform. First, the targets are detected using fuzzy clustering, edge fusion and local texture energy. The position and the size of the detected targets are then used to initialize the tracking algorithm. For each(More)
The mapping that relates the image irradiance to the image brightness (intensity) is known as the Radiometric Response Function or Camera Response Function. This usually unknown mapping is nonlinear and varies from one color channel to another. In this paper, we present a method to estimate the radiometric response functions (of R, G and B channels) of a(More)
This paper describes how model-specific constraints and domain specific knowledge can be utilized to develop efficient sampling based algorithms for robust model estimation in the presence of out-liers. As a special case, a robust algorithm for homography estimation is proposed that exploits the invariance of collinearity under homogra-phy to improve(More)
We propose a novel method to model and learn the scene activity, observed by a static camera. The proposed model is very general and can be applied for solution of a variety of problems. The motion patterns of objects in the scene are modeled in the form of a multivariate nonparametric probability density function of spatiotemporal variables (object(More)