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We propose a tracking algorithm based on a combination of Particle Filter and Mean Shift, and enhanced with a new adaptive state transition model. Particle Filter is robust to partial and total occlusions, can deal with multi-modal pdf s and can recover lost tracks. However, its complexity dramatically increases with the dimensionality of the sampled pdf.(More)
A novel approach to shadow detection is presented in this paper. The method is based on the use of invariant color models to identify and to classify shadows in digital images. The procedure is divided into two levels: first, shadow candidate regions are extracted; then, by using the invariant color features, shadow candidate pixels are classified as self(More)
—We present an accurate and robust framework for detecting and segmenting faces, localizing landmarks and achieving fine registration of face meshes based on the fitting of a facial model. This model is based on a 3D Point Distribution Model (PDM) that is fitted without relying on texture, pose or orientation information. Fitting is initialized using(More)
1 Abstract We propose an accurate tracking algorithm based on a multi-feature statistical model. The model combines in a single particle filter colour and gradient-based orientation information. A reliability measure derived from the particle distribution is used to adaptively weigh the contribution of the two features. Furthermore, information from the(More)
Local illumination changes due to shadows often reduce the quality of object-based video composition and mislead object recognition. This problem makes shadow detection a desirable tool for a wide range of applications, such as video production and visual surveillance. In this paper, we present an algorithm for the isolation of video objects from the local(More)
We propose a tracking algorithm that combines the Mean Shift search in a Particle Filtering framework and a target representation that uses multiple semi-overlapping color histograms. The target representation introduces spatial information that accounts for rotation and anisotropic scaling without compromising the flexibility typical of color histograms.(More)
We present a novel algorithm for automated video production based on content ranking. The proposed algorithm generates videos by performing camera selection while minimizing the number of inter-camera switches. We model the problem as a finite horizon Partially Observable Markov Decision Process over temporal windows and we use a multivariate Gaussian(More)
—We propose a filtering framework for multi-target tracking that is based on the Probability Hypothesis Density (PHD) filter and data association using graph matching. This framework can be combined with any object detectors that generate positional and dimensional information of objects of interest. The PHD filter compensates for missing detections and(More)