Alshehri Mohammed, Zahoor Laiba, AlQahtani Yahya, Alshahrani Abdulmonem, AlHammadi Dina Abdulaziz, Jalal Ahmad, Liu Hui
Department of Computer Science, King Khalid University, Abha, Saudi Arabia.
Faculty of Computer Science, Air University, Islamabad, Pakistan.
Front Neurorobot. 2025 Apr 17;19:1582995. doi: 10.3389/fnbot.2025.1582995. eCollection 2025.
Understanding human actions in complex environments is crucial for advancing applications in areas such as surveillance, robotics, and autonomous systems. Identifying actions from UAV-recorded videos becomes more challenging as the task presents unique challenges, including motion blur, dynamic background, lighting variations, and varying viewpoints. The presented work develops a deep learning system that recognizes multi-person behaviors from data gathered by UAVs. The proposed system provides higher recognition accuracy while maintaining robustness along with dynamic environmental adaptability through the integration of different features and neural network models. The study supports the wider development of neural network systems utilized in complicated contexts while creating intelligent UAV applications utilizing neural networks.
The proposed study uses deep learning and feature extraction approaches to create a novel method to recognize various actions in UAV-recorded video. The proposed model improves identification capacities and system robustness by addressing motion dynamic problems and intricate environmental constraints, encouraging advancements in UAV-based neural network systems.
We proposed a deep learning-based framework with feature extraction approaches that may effectively increase the accuracy and robustness of multi-person action recognition in the challenging scenarios. Compared to the existing approaches, our system achieved 91.50% on MOD20 dataset and 89.71% on Okutama-Action. These results do, in fact, show how useful neural network-based methods are for managing the limitations of UAV-based application.
Results how that the proposed framework is indeed effective at multi-person action recognition under difficult UAV conditions.
理解复杂环境中的人类行为对于推进监控、机器人技术和自主系统等领域的应用至关重要。从无人机录制的视频中识别动作变得更具挑战性，因为该任务存在独特的挑战，包括运动模糊、动态背景、光照变化和不同视角。本文提出的工作开发了一种深度学习系统，可从无人机收集的数据中识别多人行为。通过整合不同特征和神经网络模型，该系统在保持鲁棒性和动态环境适应性的同时提供了更高的识别准确率。这项研究支持在复杂环境中使用的神经网络系统的更广泛发展，同时利用神经网络创建智能无人机应用。
本研究采用深度学习和特征提取方法，创建了一种识别无人机录制视频中各种动作的新方法。该模型通过解决运动动态问题和复杂的环境约束来提高识别能力和系统鲁棒性，推动基于无人机的神经网络系统的发展。
我们提出了一个基于深度学习的框架及特征提取方法，在具有挑战性的场景中可以有效提高多人动作识别的准确率和鲁棒性。与现有方法相比，我们的系统在MOD20数据集上达到了91.50%的准确率，在奥多摩动作数据集上达到了89.71%的准确率。这些结果确实表明了基于神经网络的方法在应对基于无人机应用的局限性方面的有效性。
结果表明，所提出的框架在困难的无人机条件下进行多人动作识别方面确实有效。
