1 research outputs found
ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition
The ChaLearn large-scale gesture recognition challenge has been run twice in
two workshops in conjunction with the International Conference on Pattern
Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV)
2017, attracting more than teams round the world. This challenge has two
tracks, focusing on isolated and continuous gesture recognition, respectively.
This paper describes the creation of both benchmark datasets and analyzes the
advances in large-scale gesture recognition based on these two datasets. We
discuss the challenges of collecting large-scale ground-truth annotations of
gesture recognition, and provide a detailed analysis of the current
state-of-the-art methods for large-scale isolated and continuous gesture
recognition based on RGB-D video sequences. In addition to recognition rate and
mean jaccard index (MJI) as evaluation metrics used in our previous challenges,
we also introduce the corrected segmentation rate (CSR) metric to evaluate the
performance of temporal segmentation for continuous gesture recognition.
Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM)
baseline method, determining the video division points based on the skeleton
points extracted by convolutional pose machine (CPM). Experiments demonstrate
that the proposed Bi-LSTM outperforms the state-of-the-art methods with an
absolute improvement of (from to ) of CSR.Comment: 14 pages, 8 figures, 6 table