论文标题
使用深处神经模糊的重复注意模型的人类行动表现
Human Action Performance using Deep Neuro-Fuzzy Recurrent Attention Model
论文作者
论文摘要
许多计算机视觉出版物都重点是区分人类行动识别和分类，而不是执行的动作强度。由于视频输入中存在的不确定性和信息缺乏，索引确定人类行动表现的强度是一项艰巨的任务。为了解决这种不确定性，在本文中，我们将模糊逻辑规则与基于神经的动作识别模型相连，以评估人类行动的强度为强度或轻度。在我们的方法中，我们使用时空的LSTM来生成模糊模型的权重，然后通过实验证明对动作强度的索引是可能的。我们通过将其应用于具有不同动作强度的人类行动的视频来分析集成模型，并能够在我们的强度索引生成的数据集上获得89.16％的精度。综合模型展示了神经模糊的推理模块有效估计人类作用强度指数的能力。
A great number of computer vision publications have focused on distinguishing between human action recognition and classification rather than the intensity of actions performed. Indexing the intensity which determines the performance of human actions is a challenging task due to the uncertainty and information deficiency that exists in the video inputs. To remedy this uncertainty, in this paper we coupled fuzzy logic rules with the neural-based action recognition model to rate the intensity of a human action as intense or mild. In our approach, we used a Spatio-Temporal LSTM to generate the weights of the fuzzy-logic model, and then demonstrate through experiments that indexing of the action intensity is possible. We analyzed the integrated model by applying it to videos of human actions with different action intensities and were able to achieve an accuracy of 89.16% on our intensity indexing generated dataset. The integrated model demonstrates the ability of a neuro-fuzzy inference module to effectively estimate the intensity index of human actions.
