Abstract This research paper introduces a new approach for human activity recognition from UAV-captured video sequences. The proposed approach involves two phases: an offline phase and an inference phase. A scene stabilization step is performed together with these two phases. The offline phase aims to generate the human/non-human model as well as a human activity model using a convolutional neural network. The inference phase makes use of the already generated models in order to detect humans and recognize their activities. Our main contribution lies in adapting the convolutional neural networks, normally dedicated to the classification task, to detect humans. In addition, the classification of human activities is carried out according to two scenarios: An instant classification of video frames and an entire classification of the video sequences. Relying on an experimental evaluation of the proposed methods for human detection and human activity classification on the UCF-ARG dataset, we validated not only these contributions but also the performance of our methods compared to the existing ones.

中文翻译：

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从无人机捕获的视频序列中识别人类活动

摘要 本研究论文介绍了一种从无人机捕获的视频序列中识别人类活动的新方法。所提出的方法涉及两个阶段：离线阶段和推理阶段。场景稳定步骤与这两个阶段一起执行。离线阶段旨在使用卷积神经网络生成人类/非人类模型以及人类活动模型。推理阶段利用已经生成的模型来检测人类并识别他们的活动。我们的主要贡献在于调整通常专用于分类任务的卷积神经网络来检测人类。此外，人类活动的分类是根据两种场景进行的：视频帧的即时分类和视频序列的整体分类。