Automatic activity recognition plays an important role in addressing the efficiency issue of site management. In recent years, there has been an increasing interest in vision‐based activity recognition, while its relatively low recognition accuracy and speed impede the practical application. This paper introduces a discriminative model to combine deep activity features and contextual information to improve the recognition of activities of workers on foot in site surveillance videos. Specifically, a conditional random field (CRF) model is designed based on deep activity features, which are extracted with a single‐stream deep activity recognition network, and spatial relevance, which are obtained with a tracking‐by‐detection multiple‐object tracking method. We have evaluated various deep activity features, including action features, activity features, and joint features. Also, we have parameterized the contextual information of activities in terms of spatial relevance and represent the context with graphs of K‐nearest neighbors. The experimental results show that the CRF model based on deep activity features and activity context can significantly improve activity recognition performance to 98.77% average accuracy by 22.10% from the baseline 77.67%, which is obtained using the single‐stream deep activity recognition network, with a small computational overhead of 0.025 ms per segment.

中文翻译：

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结合深度特征和活动上下文，以提高对团队中工人活动的认识

自动活动识别在解决站点管理的效率问题中起着重要作用。近年来，人们对基于视觉的活动识别越来越感兴趣，而其相对较低的识别准确性和速度却阻碍了其实际应用。本文介绍了一种区分模型，该模型将深度活动功能和上下文信息相结合，以提高对现场监控视频中工人步行活动的识别能力。具体来说，基于深度活动特征设计条件随机场（CRF）模型，该特征是通过单流深度活动识别网络提取的，而空间相关性是通过逐检测多目标跟踪方法获得的。我们评估了各种深度活动功能，包括动作功能，活动功能和联合功能。此外，我们已经根据空间相关性对活动的上下文信息进行了参数化，并使用K近邻。实验结果表明，基于深度活动特征和活动上下文的CRF模型可以将活动识别性能从单流深度活动识别网络获得的平均识别准确度从基线77.67％提高到98.77％，平均准确度达到22.10％。每段0.025毫秒的小计算开销。