The ChaLearn large-scale gesture recognition challenge has 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 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. It describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. In this article, we discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition and provide a detailed analysis of the current methods for large-scale isolated and continuous gesture recognition. In addition to the recognition rate and mean Jaccard index (MJI) as evaluation metrics used in previous challenges, we 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) method, determining video division points based on skeleton points. Experiments show that the proposed Bi-LSTM outperforms state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR.

中文翻译：

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ChaLearn 看人：IsoGD 和 ConGD 大规模 RGB-D 手势识别

ChaLearn 大规模手势识别挑战在 2016 年国际模式识别会议 (ICPR) 和 2017 年计算机视觉国际会议 (ICCV) 的两个研讨会上举办了两次，吸引了全球 200 多个团队。这个挑战有两条赛道，分别侧重于孤立和连续的手势识别。它描述了两个基准数据集的创建，并基于这两个数据集分析了大规模手势识别的进展。在本文中，我们讨论了收集手势识别的大规模真实注释的挑战，并详细分析了当前大规模孤立和连续手势识别的方法。除了识别率和平均 Jaccard 指数 (MJI) 作为先前挑战中使用的评估指标外，我们还引入了校正分割率 (CSR) 指标来评估时间分割在连续手势识别中的性能。此外，我们提出了一种双向长短期记忆（Bi-LSTM）方法，根据骨架点确定视频分割点。实验表明，所提出的 Bi-LSTM 优于最先进的方法，CSR 的绝对改进为 8.1%（从 0.8917 到 0.9639）。