Code and data

 

Support Vector Machines with Time Series Distance Kernels for Action Classification. Code

bagheri2016supportMohammad Ali Bagheri, Qigang Gao, Sergio Escalera, Support Vector Machines with Time Series Distance Kernels for Action Classification, WACV, 2016.

A new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation.

 

Tri-modal RGB-Depth-Thermal dataset for human analysis

Database description:The dataset features a total of 5724 annotated frames divided in three indoor scenes.

Activity in scene 1 and 3 is using the full depth range of the Kinect for XBOX 360 sensor whereas activity in scene 2 is constrained to a depth range of plus/minus 0.250 m in order to suppress the parallax between the two physical sensors. Scene 1 and 2 are situated in a closed meeting room with little natural light to disturb the depth sensing, whereas scene 3 is situated in an area with wide windows and a substantial amount of sunlight. For each scene, a total of three persons are interacting, reading, walking, sitting, reading, etc.Every person is annotated with a unique ID in the scene on a pixel-level in the RGB modality. For the thermal and depth modalities, annotations are transferred from the RGB images using a registration algorithm found in registrator.cpp.

Reference: Palmero, C., Clapés, A., Bahnsen, C., Møgelmose, A., Moeslund, T. B., & Escalera, S. (2016). Multi-modal RGB–Depth–Thermal Human Body Segmentation. International Journal of Computer Vision, pp 1-23.

 

Continuous Supervised Descent Method for Facial Landmark Localisation

CSDM

Reference: Marc Oliu, Ciprian Corneanu, Laszlo A. Jeni, Jeff rey F. Cohn, Takeo Kanade, and Sergio Escalera, Continuous Supervised Descent Method for Facial Landmark Localisation, ACCV 2016.  Slides. Poster. Oral. Code and data webpage.

 
 
 
 

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Reference: Sergio Escalera, Oriol Pujol, and Petia Radeva, Error-Correcting Output Codes Library, Journal of Machine Learning Research, vol. 11, pp. 661-664, MIT Press, USA, ISSN 1532-4435, IF JCR CCIA 2.789 2009 18/103, 2010. Open Source Library,Machine Learning Open Source Software.

 

 

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ChaLearn Looking at People 2014 data sets

Data sets:
http://sunai.uoc.edu/chalearnLAP/ChaLearn LaP projects: http://gesture.chalearn.org/

 

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Sergio Escalera, Xavier Baro, Jordi Gonzalez, Miguel A. Bautista, Meysam Madadi, Miguel Reyes, Víctor Ponce, Hugo J. Escalante, Jamie Shotton, Isabelle Guyon, ChaLearn Looking at People Challenge 2014: Dataset and Results, ChaLearn Looking at People, European Conference on Computer Vision, 2014.
Daniel Sánchez, Miguel Ángel Bautista, and Sergio Escalera, HuPBA 8k+: Dataset and ECOC-GraphCut based Segmentation of Human Limbs, Neurocomputing, 2014.

 

Spherical Blurred Shape Model descriptor

Code
manos3

Reference: Oscar Lopes, Miguel Reyes, Sergio Escalera, and Jordi Gonzàlez, Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments, IEEE Transactions on System Man and Cybernetics, Part B Cybernetics, 2014.

 

3D ASL Sign Language Hand Pose Recognition dataset

Dataset

HandPoseASLDB

Reference: Oscar Lopes, Miguel Reyes, Sergio Escalera, and Jordi Gonzàlez, Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments, IEEE Transactions on System Man and Cybernetics, Part B Cybernetics, 2014.

 

HuPBA-90 data set

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Video sample
Reference: Daniel Sánchez, Juan Carlos Ortega, Miguel Ángel Bautista, and Sergio Escalera, Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization, 6th Iberian Conference on Pattern Recognition and Image Analysis, IBPRIA, Madeira, 2013.

 

ChaLearn-HuPBA Multi-Modal Gesture Recognition Challenge 2013 data set
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Data set webpage

Reference: S. Escalera, J. Gonzàlez, X. Baró, M. Reyes, O. Lopes, I. Guyon, V. Athitsos, and H.J. Escalante, Multi-modal Gesture Recognition Challenge 2013: Dataset and Results, ICMI, 2013. Video sample of gesture categories and data modalities.

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HumanLimb data set

227 images from 25 different people and different background complexity. 14 limbs are labeled per image.

Reference: Antonio Hernández-Vela, Miguel Reyes, Víctor Ponce, and Sergio Escalera, GrabCut-Based Human Segmentation in Video Sequences, Sensors, Volume 12, Issue 11, 15376-15393; doi: 10.3390/s121115376, 2012.

 

 

3D human pose data

This dataset contains labelled body parts in videos recorded with Kinect camera (RGB+Depth).
Readme
Data
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Reference: Antonio Hernández-Vela, Nadezhda Zlateva, Alexander Marinov, Miguel Reyes, Petia Radeva, Dimo Dimov, and Sergio Escalera, Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps, IEEE Computer Vision and Pattern Recognition conference, 16/06/2012-21/06/2012, Providence, Rhode Island, 2012.

Reference: Antonio Hernández-Vela, Nadezhda Zlateva, Alexander Marinov, Miguel Reyes, Petia Radeva, Dimo Dimov, and Sergio Escalera, Human Limb Segmentation in Depth Maps based on Spatio-Temporal Graph Cuts Optimization, Journal of Ambient Intelligence and Smart Environments JAISE, 2012.

 

Cover data set for text detection

Includes more than 15000 images and a subset labeled in xml.

Reference: Sergio Escalera, Xavier Baró, Jordi Vitrià and Petia Radeva, Text Detection in Urban Scenes, International Conference of the “Associació Catalana d’Intel·ligència Artificial”, CCIA 2009.

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Symbols in natural scenes data set

This data set includes about 550 images of 17 different symbols that appear in natural scenes.

Reference: Sergio Escalera, Alicia Fornés, Oriol Pujol, Petia Radeva, Gemma Sánchez, and Josep Lladós, Blurred Shape Model for Binary and Grey-level Symbol Recognition, Pattern Recognition Letters, doi:10.1016/j.patrec.2009.08.001, 2009.

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