College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf 2014, Saudi Arabia.
Comput Intell Neurosci. 2022 Jan 31;2022:8222388. doi: 10.1155/2022/8222388. eCollection 2022.
Human activity recognition (HAR) is a fascinating and significant challenging task. Generally, the accuracy of HAR systems relies on the best features from the input frames. Mostly, the activity frames have the hostile noisy conditions that cannot be handled by most of the existing edge operators. In this paper, we have designed an adoptive feature extraction method based on edge detection for HAR systems. The proposed method calculates the direction of the edges under the presence of nonmaximum conquest. The benefits are in ease that depends upon the modest procedures, and the extension possibility is to determine other types of features. Normally, it is practical to extract extra low-level information in the form of features when determining the shapes and to get the appropriate information, the additional cultured shape detection procedure is utilized or discarded. Basically, this method enlarges the percentage of the product of the signal-to-noise ratio (SNR) and the highest isolation along with localization. During the processing of the frames, again some edges are demonstrated as a footstep function; the proposed approach might give better performance than other operators. The appropriate information is extracted to form feature vector, which further be fed to the classifier for activity recognition. We assess the performance of the proposed edge-based feature extraction method under the depth dataset having thirteen various kinds of actions in a comprehensive experimental setup.
人体活动识别 (HAR) 是一项引人入胜且极具挑战性的任务。通常，HAR 系统的准确性依赖于输入帧中的最佳特征。大多数情况下，活动帧具有恶劣的嘈杂条件，这是大多数现有边缘算子无法处理的。在本文中，我们设计了一种基于边缘检测的自适应特征提取方法，用于 HAR 系统。所提出的方法在存在非最大值征服的情况下计算边缘的方向。其优点是依赖于适度的过程，并且扩展的可能性是确定其他类型的特征。通常，在确定形状时以特征的形式提取额外的低级信息是实用的，为了获得适当的信息，会利用或舍弃额外的形状检测过程。基本上，这种方法会扩大信号噪声比 (SNR) 与最高隔离和定位的乘积的百分比。在处理帧时，再次展示了一些边缘作为步阶函数；与其他算子相比，所提出的方法可能会有更好的性能。提取适当的信息以形成特征向量，然后将其进一步输入到分类器中以进行活动识别。我们在深度数据集下评估了基于边缘的特征提取方法的性能，该数据集在全面的实验设置中包含十三种不同的动作。
