Related papers: UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV

UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV

URL: http://arxiv.org/abs/2208.06702v1
Date: Sat, 13 Aug 2022 18:28:37 GMT
Title: UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV
Authors: Mahieyin Rahmun, Tonmoay Deb, Shahriar Ali Bijoy, Mayamin Hamid Raha
Abstract summary: Video datasets that capture violent and non-violent human activity from aerial point-of-view are scarce. We propose a novel, baseline simulator which is capable of generating synthetic images of crowds engaging in various activities that can be categorized as violent or non-violent. 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.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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