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Josep M. Gonfaus; Marco Pedersoli; Jordi Gonzalez; Andrea Vedaldi; Xavier Roca |
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Title |
Factorized appearances for object detection |
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Year |
2015 |
Publication |
Computer Vision and Image Understanding |
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CVIU |
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138 |
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92–101 |
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Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts |
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Abstract |
Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.
A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure.
Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories. |
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ISE; 600.063; 600.078 |
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Admin @ si @ GPG2015 |
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2705 |
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Author |
Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca |
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Title |
Human Action Recognition Using an Ensemble of Body-Part Detectors |
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2013 |
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Expert Systems |
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EXSY |
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30 |
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2 |
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101-114 |
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Human action recognition;body-part detection;hidden Markov model |
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This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body-part detectors, followed by grouping of part detections, to perform human identification. Three example-based body-part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self-occlusions through the use of ten sub-classifiers that detect body parts over a specific range of viewpoints. Each sub-classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint-invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state-of-the-art methods. |
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ISE |
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Admin @ si @ CBG2013 |
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1809 |
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Dani Rowe; Jordi Gonzalez; Marco Pedersoli; Juan J. Villanueva |
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Title |
On Tracking Inside Groups |
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2010 |
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Machine Vision and Applications |
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MVA |
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21 |
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2 |
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113–127 |
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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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Springer-Verlag |
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0932-8092 |
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ISE |
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ISE @ ise @ RGP2010 |
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1158 |
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Author |
A. Gijsenij; Theo Gevers; Joost Van de Weijer |
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Title |
Generalized Gamut Mapping using Image Derivative Structures for Color Constancy |
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2010 |
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International Journal of Computer Vision |
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IJCV |
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86 |
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2-3 |
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127-139 |
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The gamut mapping algorithm is one of the most promising methods to achieve computational color constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n-jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated colors and diffuse light. Further, as the n-jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and realworld scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n-jet based gamut mapping, (3) the combination of the different n-jet based gamut |
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Kluwer Academic Publishers Hingham, MA, USA |
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0920-5691 |
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ISE |
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CAT @ cat @ GGW2010 |
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1274 |
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Author |
Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon |
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Title |
Looking at People Special Issue |
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Journal Article |
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Year |
2018 |
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International Journal of Computer Vision |
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IJCV |
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126 |
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2-4 |
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141-143 |
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HUPBA; ISE; 600.119 |
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Admin @ si @ EGJ2018 |
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3093 |
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