Imperial College London

Dr Guillermo Garcia-Hernando

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Visiting Researcher



g.garcia-hernando Website




1009eElectrical EngineeringSouth Kensington Campus





Publication Type

7 results found

Garcia-Hernando G, Yuan S, Baek S, Kim T-Ket al., 2018, First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations, 31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 409-419, ISSN: 1063-6919

Conference paper

Yuan S, Garcia-Hernando G, Stenger B, Moon G, Chang JY, Lee KM, Molchanov P, Kautz J, Honari S, Ge L, Yuan J, Chen X, Wang G, Yang F, Akiyama K, Wu Y, Wan Q, Madadi M, Escalera S, Li S, Lee D, Oikonomidis I, Argyros A, Kim T-Ket al., 2018, Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals, 31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 2636-2645, ISSN: 1063-6919

Conference paper

Serrano I, Deniz O, Bueno G, Garcia-Hernando G, Kim T-Ket al., 2018, Spatio-temporal elastic cuboid trajectories for efficient fight recognition using Hough forests, MACHINE VISION AND APPLICATIONS, Vol: 29, Pages: 207-217, ISSN: 0932-8092

Journal article

Garcia-Hernando G, Kim T-K, 2017, Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection, 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 407-415, ISSN: 1063-6919

Conference paper

Chang HJ, Garcia-Hernando G, Tang D, Kim T-Ket al., 2016, Spatio-Temporal Hough Forest for efficient detection-localisation-recognition of fingerwriting in egocentric camera, Computer Vision and Image Understanding, Vol: 148, Pages: 87-96, ISSN: 1090-235X

Recognising fingerwriting in mid-air is a useful input tool for wearable egocentric camera. In this paper we propose a novel framework to this purpose. Specifically, our method first detects a writing hand posture and locates the position of index fingertip in each frame. From the trajectory of the fingertip, the written character is localised and recognised simultaneously. To achieve this challenging task, we first present a contour-based view independent hand posture descriptor extracted with a novel signature function. The proposed descriptor serves both posture recognition and fingertip detection. As to recognising characters from trajectories, we propose Spatio-Temporal Hough Forest that takes sequential data as input and perform regression on both spatial and temporal domain. Therefore our method can perform character recognition and localisation simultaneously. To establish our contributions, a new handwriting-in-mid-air dataset with labels for postures, fingertips and character locations is proposed. We design and conduct experiments of posture estimation, fingertip detection, character recognition and localisation. In all experiments our method demonstrates superior accuracy and robustness compared to prior arts.

Journal article

Garcia-Hernando G, Chang H, Serrano I, Deniz O, Kim Tet al., 2016, Transition Hough Forest for Trajectory-based Action Recognition, IEEE Winter Conference on Applications of Computer Vision (WACV)

Conference paper

Hameed MZ, Garcia-Hernando G, Kim T-K, 2015, Novel Spatio-temporal Features for Fingertip Writing Recognition in Egocentric Viewpoint, 14th IAPR International Conference on Machine Vision Applications (MVA), Publisher: IEEE, Pages: 484-488

Conference paper

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