ChaLearn Looking at People Challenge 2014: Dataset and Results

By Sergio Escalera, Xavier Baró, Jordi Gonzàlez, Miguel Angel Bautista, Meysam Madadi, Miguel Reyes, Víctor Ponce-López, Hugo Jair Escalante, Jamie Shotton, Isabelle M Guyon. 2014

239 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction

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Citations in the corpus

RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, background): “ChaLearn2014 Multimodal Gesture Recognition ChaLearn2014 Multimodal Gesture Recognition [29] (http://gesture.”
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks (2017, methodology): “The proposed model is evaluated on three datasets: NTU RGB+D dataset [37], SBU Interaction dataset [53], and ChaLearn Gesture Recognition dataset [9, 8].”
RGBD Datasets: Past, Present and Future (2016, background): “Montalbano Gesture [26] 27 20 13858 X Italian hand gestures ’14”
Motion Recognition Employing Multiple Kernel Learning of Fisher Vectors Using Local Skeleton Features (2015, background): “For more information about the dataset, refer to [6].”
Do Convolutional Neural Networks Learn Class Hierarchy? (2017, methodology): “In machine-learning literature, confusion matrices are often generated using the default Jet color map in MATLAB [22, 32, 46].”
ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition (2016, methodology): “The ChaLearn Multi-modal Gesture Dataset [6, 3] has more than 13 thousands gestures and 387 training samples per class, but it only has 20 classes.”
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video (2015, methodology): “In this work, we explore a variety of end-to-end trainable deep networks for video classiﬁcation applied to frame-wise gesture recognition with the Montalbano dataset that was introduced in the ChaLearn LAP 2014 Challenge [4].”
ChaLearn looking at people: A review of events and resources (2017, background): “2014 Looking at People ECCV Challenge: ChaLearn organized in 2014 three parallel challenge tracks on Human Pose Recovery on RGB data, action/interaction spotting on RGB data, and gesture spotting on RGB-Depth data [5].”
Challenges in multimodal gesture recognition (2016, methodology): “3 ChaLearn Multimodal Gesture Spotting Challenge 2014 In ChaLearn LAP 2014 (Escalera et al., 2014) we focused on the user-independent automatic spotting of a vocabulary of 20 Italian cultural/anthropological signs in image sequences, see Figure 6.”
Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition (2016, background): “ConGD consists of 47,933 gesture samples belonging to 249 gesture classes, making it the largest user-independent dataset [27] surpassing the ChaLearn 2014 Gesture Recognition Dataset [22] which has 13,858 samples and the DeviSign Chinese Sign Language Dataset which has 24,000 samples [28].”
ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016 (2016, methodology): “Following previous series on Looking at People (LAP) competitions [14, 13, 9, 12, 4, 10, 42, 15], in 2016, ChaLearn and Faces of the World organized new competitions and workshops on automatic face analysis in still images.”
Deep Label Distribution Learning With Label Ambiguity (2016, methodology): “org/matconvnet/ The second dataset is from the apparent age estimation competition, the first competition track of the ICCV ChaLearn LAP 2015 workshop [20].”
Multi-scale Deep Learning for Gesture Detection and Localization (2014, methodology): “The gestures are drawn from a large vocabulary, from which 20 categories are identified to detect and recognize [12].”