一种用于大型活动拥挤入口处推挤行为早期检测的云端深度学习框架
A Cloud-based Deep Learning Framework for Early Detection of Pushing at Crowded Event Entrances
AI总结：
针对大型活动拥挤入口推挤行为难以及早发现的问题，提出结合改进EfficientNetV2B0与深度光流模型的云端检测框架，经自建真实场景数据集验证，推挤检测准确率达87%，可助力安保早期干预。
摘要：
大型活动入口处的人群拥挤可能引发危及生命的危急状况，尤其是当人们为了更快进入活动现场而开始互相推挤时。自动、及时地识别推挤行为，有助于活动组织者和安保人员及早干预，缓解危险局势。本文提出一种基于云端的深度学习框架，用于自动早期检测大型活动拥挤入口处的推挤行为。该框架首先对EfficientNetV2B0卷积神经网络模型进行改进和训练，随后将适配后的模型与一款精准快速的预训练深度光流模型结合，采用色轮法分析视频流，实时识别推挤区域。此外，该框架利用实时采集技术和云端环境，实时收集人群视频流并给出早期检测结果。研究基于5项真实世界实验及其对应的真值数据构建了一个全新数据集，用于训练适配后的EfficientNetV2B0模型。实验设置模拟了大型活动的拥挤入口场景，每组视频实验的真值均由社会心理学家人工标注。研究在视频和生成的数据集上开展了多项实验，以评估所提框架的准确率和标注延迟时间。实验结果表明，该框架能在合理的延迟时间内识别推挤行为，准确率达87%。
英文摘要：
Crowding at the entrances of large events may lead to critical and life-threatening situations, particularly when people start pushing each other to reach the event faster. Automatic and timely identification of pushing behavior would help organizers and security forces to intervene early and mitigate dangerous situations. In this paper, we propose a cloud-based deep learning framework for automatic early detection of pushing in crowded event entrances. The proposed framework initially modifies and trains the EfficientNetV2B0 Convolutional Neural Network model. Subsequently, it integrates the adapted model with an accurate and fast pre-trained deep optical flow model with the color wheel method to analyze video streams and identify pushing patches in real-time. Moreover, the framework uses live capturing technology and a cloud-based environment to collect video streams of crowds in real-time and provide early-stage results. A novel dataset is generated based on five real-world experiments and their associated ground truth data to train the adapted EfficientNetV2B0 model. The experimental setups simulated a crowded event entrance, while the ground truths for each video experiment was generated manually by social psychologists. Several experiments on the videos and the generated dataset are carried out to evaluate the accuracy and annotation delay time of the proposed framework. The experimental results show that the proposed framework identified pushing behaviors with an accuracy rate of 87% within a reasonable delay time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
