论文标题
无与伦比的牛排：从无人机的角度来看，暴力和非暴力人群活动模拟器
UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV
论文作者
论文摘要
近年来，无人驾驶飞机（UAV）已获得了大量的吸引力，尤其是监视的背景。但是，从空中观察点捕获暴力和非暴力人类活动的视频数据集很少。为了解决这个问题，我们提出了一个新颖的基线模拟器，该模拟器能够生成参与各种活动的人群的光真实合成图像，这些序列可以归类为暴力或非暴力。人群组用使用语义分割自动计算的边界框注释。我们的模拟器能够产生大型的随机城市环境，并且能够在中端计算机上平均每秒保持25帧，并具有150个并发的人群相互作用。我们还表明，当来自所提出的模拟器的合成数据随着现实世界数据增强时，在两个不同模型中，二进制视频分类精度的平均提高了5％。
Unmanned Aerial Vehicle (UAV) has gained significant traction in the recent years, particularly the context of surveillance. However, video datasets that capture violent and non-violent human activity from aerial point-of-view is scarce. To address this issue, we propose a novel, baseline simulator which is capable of generating sequences of photo-realistic synthetic images of crowds engaging in various activities that can be categorized as violent or non-violent. The crowd groups are annotated with bounding boxes that are automatically computed using semantic segmentation. Our simulator is capable of generating large, randomized urban environments and is able to maintain an average of 25 frames per second on a mid-range computer with 150 concurrent crowd agents interacting with each other. We also show that when synthetic data from the proposed simulator is augmented with real world data, binary video classification accuracy is improved by 5% on average across two different models.
