Abstract
Crowd gatherings at social and cultural events are increasing in leaps and bounds with the increase in population. Surveillance through computer vision and expert decision making systems can help to understand the crowd phenomena at large gatherings. Understanding crowd phenomena can be helpful in early identification of unwanted incidents and their prevention. Motion flow is one of the important crowd phenomena that can be instrumental in describing the crowd behavior. Flows can be useful in understanding instabilities in the crowd. However, extracting motion flows is a challenging task due to randomness in crowd movement and limitations of the sensing device. Moreover, low-level features such as optical flow can be misleading if the randomness is high. In this paper, we propose a new model based on Langevin equation to analyze the linear dominant flows in videos of densely crowded scenarios. We assume a force model with three components, namely external force, confinement/drift force, and disturbance force. These forces are found to be sufficient to describe the linear or near-linear motion in dense crowd videos. The method is significantly faster as compared to existing popular crowd segmentation methods. The evaluation of the proposed model has been carried out on publicly available datasets as well as using our dataset. It has been observed that the proposed method is able to estimate and segment the linear flows in the dense crowd with better accuracy as compared to state-of-the-art techniques with substantial decrease in the computational overhead.
Abstract (translated)
随着人口的增加，社会文化活动中的人群聚集越来越频繁。通过计算机视觉和专家决策系统进行监控，有助于理解大型集会的人群现象。了解群体现象有助于早期识别不需要的事件及其预防。运动流是描述群体行为的重要群体现象之一。在理解人群中的不稳定性时，流程是有用的。然而，由于群体运动的随机性和传感装置的局限性，提取运动流是一项具有挑战性的任务。此外，如果随机性很高，诸如光流等低级特征可能会产生误导。本文提出了一种基于朗格文方程的新模型，用于分析密集场景视频中的线性主导流。我们假设一个由外力、约束/漂移力和干扰力三部分组成的力模型。这些力足以描述密集人群视频中的线性或近似线性运动。与现有流行的人群分割方法相比，该方法速度明显加快。已对公开可用的数据集以及使用我们的数据集对建议的模型进行了评估。据观察，与现有技术相比，该方法能够更准确地估计和分割密集人群中的线性流，同时大大降低了计算开销。
URL
https://arxiv.org/abs/1904.07233
