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Carles Fernandez, Xavier Roca, & Jordi Gonzalez. (2008). Providing Automatic Multilingual Text Generation to Artificial Cognitive Systems.
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Ariel Amato, Mikhail Mozerov, Ivan Huerta, Jordi Gonzalez, & Juan J. Villanueva. (2008). ackground Subtraction Technique Based on Chromaticity and Intensity Patterns. In 19th International Conference on Pattern Recognition, (1–4).
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Murad Al Haj, Francisco Javier Orozco, Jordi Gonzalez, & Juan J. Villanueva. (2008). Automatic Face and Facial Features Initialization for Robust and Accurate Tracking. In 19th International Conference on Pattern Recognition. (1– 4).
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Francisco Javier Orozco, & Jordi Gonzalez. (2008). Confidence Assessment on Eyelid and Eyebrow Expression Recognition. In 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2008).
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Bhaskar Chakraborty, Ognjen Rudovic, & Jordi Gonzalez. (2008). View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component-Wise HMM of Body Parts. In 8th IEEE International Conference on Automatic Face and Gesture Recognition.
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Marco Pedersoli. (2008). A Multiresolution Cascade for Human Detection.
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Bhaskar Chakraborty. (2008). View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component Wise HMM of Body Parts.
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Dani Rowe, Jordi Gonzalez, Marco Pedersoli, & Juan J. Villanueva. (2010). On Tracking Inside Groups. MVA - Machine Vision and Applications, 21(2), 113–127.
Abstract: This work develops a new architecture for multiple-target tracking in unconstrained dynamic scenes, which consists of a detection level which feeds a two-stage tracking system. A remarkable characteristic of the system is its ability to track several targets while they group and split, without using 3D information. Thus, special attention is given to the feature-selection and appearance-computation modules, and to those modules involved in tracking through groups. The system aims to work as a stand-alone application in complex and dynamic scenarios. No a-priori knowledge about either the scene or the targets, based on a previous training period, is used. Hence, the scenario is completely unknown beforehand. Successful tracking has been demonstrated in well-known databases of both indoor and outdoor scenarios. Accurate and robust localisations have been yielded during long-term target merging and occlusions.
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Ignasi Rius, Jordi Gonzalez, Javier Varona, & Xavier Roca. (2009). Action-specific motion prior for efficient bayesian 3D human body tracking. PR - Pattern Recognition, 42(11), 2907–2921.
Abstract: In this paper, we aim to reconstruct the 3D motion parameters of a human body
model from the known 2D positions of a reduced set of joints in the image plane.
Towards this end, an action-specific motion model is trained from a database of real
motion-captured performances. The learnt motion model is used within a particle
filtering framework as a priori knowledge on human motion. First, our dynamic
model guides the particles according to similar situations previously learnt. Then, the solution space is constrained so only feasible human postures are accepted as valid solutions at each time step. As a result, we are able to track the 3D configuration of the full human body from several cycles of walking motion sequences using only the 2D positions of a very reduced set of joints from lateral or frontal viewpoints.
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Mikhail Mozerov, Ariel Amato, Xavier Roca, & Jordi Gonzalez. (2009). Solving the Multi Object Occlusion Problem in a Multiple Camera Tracking System. Pattern Recognition and Image Analysis, 165–171.
Abstract: An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path.
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Pau Baiget, Carles Fernandez, Xavier Roca, & Jordi Gonzalez. (2009). Generation of Augmented Video Sequences Combining Behavioral Animation and Multi Object Tracking. Computer Animation and Virtual Worlds, 20(4), 473–489.
Abstract: In this paper we present a novel approach to generate augmented video sequences in real-time, involving interactions between virtual and real agents in real scenarios. On the one hand, real agent motion is estimated by means of a multi-object tracking algorithm, which determines real objects' position over the scenario for each time step. On the other hand, virtual agents are provided with behavior models considering their interaction with the environment and with other agents. The resulting framework allows to generate video sequences involving behavior-based virtual agents that react to real agent behavior and has applications in education, simulation, and in the game and movie industries. We show the performance of the proposed approach in an indoor and outdoor scenario simulating human and vehicle agents. Copyright © 2009 John Wiley & Sons, Ltd.
We present a novel approach to generate augmented video sequences in real-time, involving interactions between virtual and real agents in real scenarios. On the one hand, real agent motion is estimated by means of a multi-object tracking algorithm, which determines real objects' position over the scenario for each time step. On the other hand, virtual agents are provided with behavior models considering their interaction with the environment and with other agents. © 2009 Wiley Periodicals, Inc.
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Mikhail Mozerov, Ariel Amato, & Xavier Roca. (2009). Occlusion Handling in Trinocular Stereo using Composite Disparity Space Image. In 19th International Conference on Computer Graphics and Vision (69–73).
Abstract: In this paper we propose a method that smartly improves occlusion handling in stereo matching using trinocular stereo. The main idea is based on the assumption that any occluded region in a matched stereo pair (middle-left images) in general is not occluded in the opposite matched pair (middle-right images). Then two disparity space images (DSI) can be merged in one composite DSI. The proposed integration differs from the known approach that uses a cumulative cost. A dense disparity map is obtained with a global optimization algorithm using the proposed composite DSI. The experimental results are evaluated on the Middlebury data set, showing high performance of the proposed algorithm especially in the occluded regions. One of the top positions in the rank of the Middlebury website confirms the performance of our method to be competitive with the best stereo matching.
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Mikhail Mozerov, Ignasi Rius, Xavier Roca, & Jordi Gonzalez. (2010). Nonlinear synchronization for automatic learning of 3D pose variability in human motion sequences. EURASIPJ - EURASIP Journal on Advances in Signal Processing, .
Abstract: Article ID 507247
A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes.
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Jordi Gonzalez, Dani Rowe, Javier Varona, & Xavier Roca. (2009). Understanding Dynamic Scenes based on Human Sequence Evaluation. IMAVIS - Image and Vision Computing, 27(10), 1433–1444.
Abstract: In this paper, a Cognitive Vision System (CVS) is presented, which explains the human behaviour of monitored scenes using natural-language texts. This cognitive analysis of human movements recorded in image sequences is here referred to as Human Sequence Evaluation (HSE) which defines a set of transformation modules involved in the automatic generation of semantic descriptions from pixel values. In essence, the trajectories of human agents are obtained to generate textual interpretations of their motion, and also to infer the conceptual relationships of each agent w.r.t. its environment. For this purpose, a human behaviour model based on Situation Graph Trees (SGTs) is considered, which permits both bottom-up (hypothesis generation) and top-down (hypothesis refinement) analysis of dynamic scenes. The resulting system prototype interprets different kinds of behaviour and reports textual descriptions in multiple languages.
Keywords: Image Sequence Evaluation; High-level processing of monitored scenes; Segmentation and tracking in complex scenes; Event recognition in dynamic scenes; Human motion understanding; Human behaviour interpretation; Natural-language text generation; Realistic demonstrators
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Carles Fernandez, Pau Baiget, Xavier Roca, & Jordi Gonzalez. (2009). Exploiting Natural Language Generation in Scene Interpretation. In Human–Centric Interfaces for Ambient Intelligence (Vol. 4, 71–93). Elsevier Science and Tech.
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