Estimation of linear motion in dense crowd videos using Langevin model

Abstract

Expert and intelligent systems are highly popular for designing cross-domain autonomous systems. Computer vision aided by expert decision-making systems are widely used to automate tasks that are normally carried out manually. For example, in the traditional way of traffic monitoring, signals are either controlled through predefined set-ups or with the help of visual observations. Even though some of the modern cities employ sensor-based surveillance at large, full automation is still far from the desirable accuracy. This is more challenging for monitoring high-density crowds that often cause unwanted situations due to sudden changes in dynamics. It has been shown in this paper that the movement of a dense crowd can be approximated using well-known physics-based models. Such a modeling can help to understand the overall crowd behavior. In accomplishing this, we have introduced a computer vision guided expert system with the help of a Langevin equation-based force model to represent the linear flow of the crowd, particularly in situations when the density is high. One of the primary contributions of our proposed model is its computational efficiency, particularly when a timely decision can help to avoid unwanted situations. Our proposed three-term force model is capable of predicting the positions of the group of key-points in a video frame leading to a significant computational gain. We have carried out several experiments on publicly available videos as well as our own videos to validate the claims in terms of accuracy as well as computational gain. It has been observed that the proposed physics-based model outperforms the existing systems with a 4−6% improvement in the segmentation accuracy. Moreover, we have achieved multi-fold computational gain. We believe the proposed work, when supported by appropriate post-processing, can be used to develop crowd monitoring applications. © 2020 Elsevier Ltd