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.

中文翻译：

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使用深度神经模糊循环注意模型的人类行为表现

大量计算机视觉出版物侧重于区分人类动作识别和分类，而不是执行动作的强度。由于视频输入中存在不确定性和信息不足，索引决定人类行为表现的强度是一项具有挑战性的任务。为了弥补这种不确定性，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结合，以将人类动作的强度评定为强烈或轻微。在我们的方法中，我们使用时空 LSTM 来生成模糊逻辑模型的权重，然后通过实验证明可以对动作强度进行索引。我们通过将集成模型应用于具有不同动作强度的人类动作视频来分析集成模型，并且能够在我们的强度索引生成数据集上实现 89.16% 的准确率。集成模型展示了神经模糊推理模块有效估计人类行为强度指数的能力。