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Ignasi Rius. (2005). Articulated 3D Human Motion Moldeling for Tracking and Reconstruction.
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Ignasi Rius. (2010). Motion Priors for Efficient Bayesian Tracking in Human Sequence Evaluation (Jordi Gonzalez, & Xavier Roca, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Recovering human motion by visual analysis is a challenging computer vision research
area with a lot of potential applications. Model-based tracking approaches, and in
particular particle lters, formulate the problem as a Bayesian inference task whose
aim is to sequentially estimate the distribution of the parameters of a human body
model over time. These approaches strongly rely on good dynamical and observation
models to predict and update con gurations of the human body according to measurements from the image data. However, it is very di cult to design observation
models which extract useful and reliable information from image sequences robustly.
This results specially challenging in monocular tracking given that only one viewpoint
from the scene is available. Therefore, to overcome these limitations strong motion
priors are needed to guide the exploration of the state space.
The work presented in this Thesis is aimed to retrieve the 3D motion parameters
of a human body model from incomplete and noisy measurements of a monocular
image sequence. These measurements consist of the 2D positions of a reduced set of
joints in the image plane. Towards this end, we present a novel action-speci c model
of human motion which is trained from several databases of real motion-captured
performances of an action, and is used as a priori knowledge within a particle ltering
scheme.
Body postures are represented by means of a simple and compact stick gure
model which uses direction cosines to represent the direction of body limbs in the 3D
Cartesian space. Then, for a given action, Principal Component Analysis is applied to
the training data to perform dimensionality reduction over the highly correlated input
data. Before the learning stage of the action model, the input motion performances
are synchronized by means of a novel dense matching algorithm based on Dynamic
Programming. The algorithm synchronizes all the motion sequences of the same
action class, nding an optimal solution in real-time.
Then, a probabilistic action model is learnt, based on the synchronized motion
examples, which captures the variability and temporal evolution of full-body motion
within a speci c action. In particular, for each action, the parameters learnt are: a
representative manifold for the action consisting of its mean performance, the standard deviation from the mean performance, the mean observed direction vectors from
each motion subsequence of a given length and the expected error at a given time
instant.
Subsequently, the action-speci c model is used as a priori knowledge on human
motion which improves the e ciency and robustness of the overall particle filtering tracking framework. First, the dynamic model guides the particles according to similar
situations previously learnt. Then, the state space is constrained so only feasible
human postures are accepted as valid solutions at each time step. As a result, the
state space is explored more e ciently as the particle set covers the most probable
body postures.
Finally, experiments are carried out using test sequences from several motion
databases. Results point out that our tracker scheme is able to estimate the rough
3D con guration of a full-body model providing only the 2D positions of a reduced
set of joints. Separate tests on the sequence synchronization method and the subsequence probabilistic matching technique are also provided.
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Ignasi Rius, Dani Rowe, Jordi Gonzalez, & Xavier Roca. (2005). A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522: 529–536.
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Ignasi Rius, Dani Rowe, Jordi Gonzalez, & Xavier Roca. (2005). 3D Action Modeling and Reconstruction for 2D Human Body Tracking.
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Ignasi Rius, Javier Varona, Jordi Gonzalez, & Juan J. Villanueva. (2006). Action Spaces for Efficient Bayesian Tracking of Human Motion.
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Ignasi Rius, Javier Varona, Xavier Roca, & Jordi Gonzalez. (2006). Posture Constraints for Bayesian Human Motion Tracking. In IV Conference on Articulated Motion and Deformable Objects (AMDO´06), LNCS 4069: 414–423.
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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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Ignasi Rius, Jordi Gonzalez, Mikhail Mozerov, & Xavier Roca. (2008). Automatic Learning of 3D Pose Variability in Walking Performances for Gait Analysis. International Journal for Computational Vision and Biomechanics, 33–43.
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Iiris Lusi, Julio C. S. Jacques Junior, Jelena Gorbova, Xavier Baro, Sergio Escalera, Hasan Demirel, et al. (2017). Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists.
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Iiris Lusi, Sergio Escalera, & Gholamreza Anbarjafari. (2016). SASE: RGB-Depth Database for Human Head Pose Estimation. In 14th European Conference on Computer Vision Workshops.
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Iiris Lusi, Sergio Escalera, & Gholamreza Anbarjafari. (2016). Human Head Pose Estimation on SASE database using Random Hough Regression Forests. In 23rd International Conference on Pattern Recognition Workshops (Vol. 10165). LNCS.
Abstract: In recent years head pose estimation has become an important task in face analysis scenarios. Given the availability of high resolution 3D sensors, the design of a high resolution head pose database would be beneficial for the community. In this paper, Random Hough Forests are used to estimate 3D head pose and location on a new 3D head database, SASE, which represents the baseline performance on the new data for an upcoming international head pose estimation competition. The data in SASE is acquired with a Microsoft Kinect 2 camera, including the RGB and depth information of 50 subjects with a large sample of head poses, allowing us to test methods for real-life scenarios. We briefly review the database while showing baseline head pose estimation results based on Random Hough Forests.
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Ikechukwu Ofodile, Ahmed Helmi, Albert Clapes, Egils Avots, Kerttu Maria Peensoo, Sandhra Mirella Valdma, et al. (2019). Action recognition using single-pixel time-of-flight detection. ENTROPY - Entropy, 21(4), 414.
Abstract: Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene.
Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47% accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent
neural network.
Keywords: single pixel single photon image acquisition; time-of-flight; action recognition
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Ilke Demir, Dena Bazazian, Adriana Romero, Viktoriia Sharmanska, & Lyne P. Tchapmi. (2018). WiCV 2018: The Fourth Women In Computer Vision Workshop. In 4th Women in Computer Vision Workshop (pp. 1941–19412).
Abstract: We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.
Keywords: Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning
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Isabel Guitart, Jordi Conesa, Luis Villarejo, Agata Lapedriza, David Masip, Antoni Perez, et al. (2013). Opinion Mining on Educational Resources at the Open University of Catalonia. In 3rd International Workshop on Adaptive Learning via Interactive, Collaborative and Emotional approaches. In conjunction with CISIS 2013: The 7th International Conference on Complex, Intelligent, and Software Intensive Systems (pp. 385–390).
Abstract: In order to make improvements to teaching, it is vital to know what students think of the way they are taught. With that purpose in mind, exhaustively analyzing the forums associated with the subjects taught at the Universitat Oberta de Cataluya (UOC) would be extremely helpful, as the university's students often post comments on their learning experiences in them. Exploiting the content of such forums is not a simple undertaking. The volume of data involved is very large, and performing the task manually would require a great deal of effort from lecturers. As a first step to solve this problem, we propose a tool to automatically analyze the posts in forums of communities of UOC students and teachers, with a view to systematically mining the opinions they contain. This article defines the architecture of such tool and explains how lexical-semantic and language technology resources can be used to that end. For pilot testing purposes, the tool has been used to identify students' opinions on the UOC's Business Intelligence master's degree course during the last two years. The paper discusses the results of such test. The contribution of this paper is twofold. Firstly, it demonstrates the feasibility of using natural language parsing techniques to help teachers to make decisions. Secondly, it introduces a simple tool that can be refined and adapted to a virtual environment for the purpose in question.
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Isabelle Guyon, Imad Chaabane, Hugo Jair Escalante, Sergio Escalera, Damir Jajetic, James Robert Lloyd, et al. (2016). A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention. In AutoML Workshop (pp. 1–8).
Abstract: The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully automatic, black-box learning machines for feature-based classification and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across different types of complexity. Over six rounds, participants succeeded in delivering AutoML software capable of being trained and tested without human intervention. Although improvements can still be made to close the gap between human-tweaked and AutoML models, this competition contributes to the development of fully automated environments by challenging practitioners to solve problems under specific constraints and sharing their approaches; the platform will remain available for post-challenge submissions at http://codalab.org/AutoML.
Keywords: AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning
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