Within this paper, we present two neural nets for view-independent complex human activity recognition (HAR) from video frames. For our study here, we reduce the number of frames produced by a video sequence given that we can identify activities from a sparsely sampled sequence of body poses, and, at the same time, we are able to reduce the processing complexity and response while hardly affecting the accuracy, precision, and recall. To do so, we use a formal framework to ensure the quality of data collection and data preprocessing. We utilize neural networks for the classification of single and complex body activities. More specifically, we consider the sequence of body poses as a time-series problem given that they can provide state-of-the-art results on challenging recognition tasks with little data engineering. Deep Learning in the form of Convolutional Neural Network (CNN), Long Short-Term Neural Network (LSTM), and a one-dimensional Convolutional Neural Network Long Short-Term Memory model (CNN-LSTM) are used as benchmarks to classify the activity.

中文翻译：

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神经网络作为复杂人类活动的分类机制

在本文中，我们提出了两个神经网络，用于从视频帧中进行独立于视图的复杂人类活动识别 (HAR)。对于我们在这里的研究，我们减少了视频序列产生的帧数，因为我们可以从稀疏采样的身体姿势序列中识别活动，同时，我们能够降低处理复杂性和响应，而几乎没有影响准确率、准确率和召回率。为此，我们使用正式的框架来确保数据收集和数据预处理的质量。我们利用神经网络对单一和复杂的身体活动进行分类。更具体地说，我们将身体姿势序列视为一个时间序列问题，因为它们可以在几乎没有数据工程的情况下为具有挑战性的识别任务提供最先进的结果。