Human action recognition is a trending topic in the field of computer vision and its allied fields. The goal of human action recognition is to identify any human action that takes place in an image or a video dataset. For instance, the actions include walking, running, jumping, throwing, and much more. Existing human action recognition techniques have their own set of limitations when it concerns model accuracy and flexibility. To overcome these limitations, deep learning technologies were implemented. In the deep learning approach, a model learns by itself to improve its recognition accuracy and avoids problems such as gradient eruption, overfitting, and underfitting. In this paper, we propose a novel parameter initialization technique using the Maxout activation function. Firstly, human action is detected and tracked from the video dataset to learn the spatial-temporal features. Secondly, the extracted feature descriptors are trained using the RBM-NN. Thirdly, the local features are encoded into global features using an integrated forward and backward propagation process via RBM-NN. Finally, an SVM classifier recognizes the human actions in the video dataset. The experimental analysis performed on various benchmark datasets showed an improved recognition rate when compared to other state-of-the-art learning models.

中文翻译：

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使用 RBM-NN 进行人类动作识别的新型参数初始化技术。


人类动作识别是计算机视觉及其相关领域的热门话题。人类动作识别的目标是识别图像或视频数据集中发生的任何人类动作。例如，动作包括走、跑、跳、投掷等等。当涉及模型准确性和灵活性时，现有的人类动作识别技术有其自身的局限性。为了克服这些限制，实施了深度学习技术。在深度学习方法中，模型通过自身学习来提高识别精度，避免梯度爆发、过拟合和欠拟合等问题。在本文中，我们提出了一种使用 Maxout 激活函数的新颖参数初始化技术。首先，从视频数据集中检测和跟踪人类动作以学习时空特征。其次，使用 RBM-NN 训练提取的特征描述符。第三，通过 RBM-NN 使用集成的前向和后向传播过程将局部特征编码为全局特征。最后，SVM 分类器识别视频数据集中的人类动作。对各种基准数据集进行的实验分析表明，与其他最先进的学习模型相比，识别率有所提高。