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Ivan Huerta; Marco Pedersoli; Jordi Gonzalez; Alberto Sanfeliu |
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Title |
Combining where and what in change detection for unsupervised foreground learning in surveillance |
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Year |
2015 |
Publication |
Pattern Recognition |
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PR |
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48 |
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3 |
Pages |
709-719 |
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Keywords |
Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance |
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Abstract |
Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art. |
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ISE; 600.063; 600.078 |
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Admin @ si @ HPG2015 |
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2589 |
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Author |
Ivan Huerta; Ariel Amato; Xavier Roca; Jordi Gonzalez |
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Title |
Exploiting Multiple Cues in Motion Segmentation Based on Background Subtraction |
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Year |
2013 |
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Neurocomputing |
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NEUCOM |
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100 |
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183–196 |
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Motion segmentation; Shadow suppression; Colour segmentation; Edge segmentation; Ghost detection; Background subtraction |
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This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motionsegmentation. In our first contribution, a case analysis of motionsegmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motionsegmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches. |
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Elsevier |
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Admin @ si @ HAR2013 |
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1808 |
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Ignasi Rius; Jordi Gonzalez; Mikhail Mozerov; Xavier Roca |
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Automatic Learning of 3D Pose Variability in Walking Performances for Gait Analysis |
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2008 |
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International Journal for Computational Vision and Biomechanics |
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1 |
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1 |
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33–43 |
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ISE |
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ISE @ ise @ RGM2008 |
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1020 |
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Ignasi Rius; Jordi Gonzalez; J. Varona; Xavier Roca |
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Title |
Action-specific motion prior for efficient bayesian 3D human body tracking |
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Journal Article |
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2009 |
Publication |
Pattern Recognition |
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PR |
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42 |
Issue |
11 |
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2907–2921 |
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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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0031-3203 |
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ISE |
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ISE @ ise @ RGV2009 |
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1159 |
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Author |
Hamdi Dibeklioglu; M.O. Hortas; I. Kosunen; P. Zuzánek; Albert Ali Salah; Theo Gevers |
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Design and implementation of an affect-responsive interactive photo frame |
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2011 |
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Journal on Multimodal User Interfaces |
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JMUI |
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4 |
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2 |
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81-95 |
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This paper describes an affect-responsive interactive photo-frame application that offers its user a different experience with every use. It relies on visual analysis of activity levels and facial expressions of its users to select responses from a database of short video segments. This ever-growing database is automatically prepared by an offline analysis of user-uploaded videos. The resulting system matches its user’s affect along dimensions of valence and arousal, and gradually adapts its response to each specific user. In an extended mode, two such systems are coupled and feed each other with visual content. The strengths and weaknesses of the system are assessed through a usability study, where a Wizard-of-Oz response logic is contrasted with the fully automatic system that uses affective and activity-based features, either alone, or in tandem. |
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Springer–Verlag |
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1783-7677 |
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ALTRES;ISE |
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Admin @ si @ DHK2011 |
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1842 |
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