Alberto Albiol

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This paper presents an approach to infer what is happening in a (crowded) scene using a statistical method. Rather than trying to segment and track the individuals in each frame, our basic idea is to detect salient points (corners) along with their motion vectors. Finally, we obtain statistical measures on this data which are highly correlated with the kind(More)
In this paper, we describe a unique new paradigm for video database management known as ViBE (Video Indexing and Browsing Environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. The system first segments video sequences into shots by using a new feature vector known as the Generalized Trace(More)
This paper presents a system that labels TV shots either as commercial or program shots. The system uses two observations: logo presence and shot duration. These observations are modeled using HMMs, and a Viterbi decoder is finally used for shot labeling. The system has been tested on several hours of real video, achieving more than 99% correct labeling.
In this paper, we present a novel algorithm for precise eye detection. First, a couple of AdaBoost classifiers trained with Haar-like features are used to preselect possible eye locations. Then, a Support Vector Machine machine that uses Histograms of Oriented Gradients descriptors is used to obtain the best pair of eyes among all possible combinations of(More)
This paper presents a video-based approach to detect the presence of parked vehicles in street lanes. Potential applications include the detection of illegally and double-parked vehicles in urban scenarios and incident detection on roads. The technique extracts information from low-level feature points (Harris corners) to create spatiotemporal maps that(More)
This paper presents a comparison between a new face recognition algorithm based on EBGM which replaces Gabor features by HOG descriptors and the original EBGM. The experiments results show a better performance behavior using public available databases. This better performance is explained by the properties of HOG descriptors which are more robust to changes(More)