Learn More
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)
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)
—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)
— We present a novel multi-feature video object tra-jectory clustering algorithm that estimates common patterns of behaviors and isolates outliers. The proposed algorithm is based on four main steps, namely the extraction of a set of representative trajectory features, non-parametric clustering, cluster merging and information fusion for the identification(More)
The absence of a commonly adopted performance evaluation framework is hampering advances in the design of effective video trackers. In this paper, we present a single-score evaluation measure and a protocol to objectively compare trackers. The proposed measure evaluates tracking accuracy and failure, and combines them for both summative and formative(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 algorithm for tracking video object which is based on a hybrid strategy. This strategy uses both object and region information to solve the correspondence problem. Low level descriptors are exploited to track object's regions and to cope with track management issues. Appearance and disappearance of objects, splitting and partial occlusions(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)