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首页> 外文期刊>IEEE transactions on multimedia >2-D Skeleton-Based Action Recognition via Two-Branch Stacked LSTM-RNNs
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2-D Skeleton-Based Action Recognition via Two-Branch Stacked LSTM-RNNs

机译：基于2-D基于骨架的动作识别通过双分支堆叠LSTM-RNNS

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摘要

Action recognition in video sequences is an interesting field for many computer vision applications, including behavior analysis, event recognition, and video surveillance. In this article, a method based on 2D skeleton and two-branch stacked Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells is proposed. Unlike 3D skeletons, usually generated by RGB-D cameras, the 2D skeletons adopted in this article are reconstructed starting from RGB video streams, therefore allowing the use of the proposed approach in both indoor and outdoor environments. Moreover, any case of missing skeletal data is managed by exploiting 3D-Convolutional Neural Networks (3D-CNNs). Comparative experiments with several key works on KTH and Weizmann datasets show that the method described in this paper outperforms the current state-of-the-art. Additional experiments on UCF Sports and IXMAS datasets demonstrate the effectiveness of our method in the presence of noisy data and perspective changes, respectively. Further investigations on UCF Sports, HMDB51, UCF101, and Kinetics400 highlight how the combination between the proposed two-branch stacked LSTM and the 3D-CNN-based network can manage missing skeleton information, greatly improving the overall accuracy. Moreover, additional tests on KTH and UCF Sports datasets also show the robustness of our approach in the presence of partial body occlusions. Finally, comparisons on UT-Kinect and NTU-RGB+D datasets show that the accuracy of the proposed method is fully comparable to that of works based on 3D skeletons.
机译：视频序列中的动作识别是许多计算机视觉应用的有趣字段，包括行为分析，事件识别和视频监控。在本文中，提出了一种基于2D骨架的方法和具有长短期存储器（LSTM）单元的两分支堆叠的经常性神经网络（RNN）。与通常由RGB-D相机产生的3D骨架不同，从RGB视频流开始，从RGB视频流开始重建本文中采用的2D骨架，因此允许在室内和室外环境中使用所提出的方法。此外，通过利用3D卷积神经网络（3D-CNN）来管理丢失骨骼数据的任何情况。在KTH和Weizmann数据集上具有几个关键作品的比较实验表明，本文中描述的方法优于当前最先进的。对UCF体育和IXMAS数据集的其他实验分别证明了我们在存在嘈杂的数据和透视变化中的方法的有效性。对UCF运动，HMDB51，UCF101和KINETICS400的进一步调查突出了所提出的双分支堆叠LSTM和3D-CNN网络之间的组合如何管理缺失的骨架信息，大大提高了整体准确性。此外，在KTH和UCF运动数据集上的额外测试也显示了在部分身体闭塞存在下我们的方法的稳健性。最后，UT-Kinect和NTU-RGB + D数据集上的比较表明，所提出的方法的准确性完全可与基于3D骨架的作品相比。

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