International Conference on Pattern Recognition是IAPR的旗舰会议、国际模式识别协会和模式识别领域的首场会议,包括计算机视觉、图像、声音、语音、传感器模式处理和机器智能。ICPR2020是这一系列的第25个项目,从开始到现在已经50岁了。ICPR 2020将是一个为期6天的活动,包括研讨会、辅导、主要会议、研究成果展示、科学竞赛和展览。它将汇集世界范围内该领域的顶尖研究人员,并为与会者提供互动和培养新思想和合作的机会。官网链接:https://www.micc.unifi.it/icpr2020/

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 $200$ 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 $8.1\%$ (from $0.8917$ to $0.9639$) of CSR.

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