Automatic crowd analysis from very high resolution satellite images

Abstract

Recently automatic detection of people crowds from images became a very important research field, since it can provide crucial information especially for police departments and crisis management teams. Due to the importance of the topic, many researchers tried to solve this problem using street cameras. However, these cameras cannot be used to monitor very large outdoor public events. In order to bring a solution to the problem, herein we propose a novel approach to detect crowds automatically from remotely sensed images, and especially from very high resolution satellite images. To do so, we use a local feature based probabilistic framework. We extract local features from color components of the input image. In order to eliminate redundant local features coming from other objects in given scene, we apply a feature selection method. For feature selection purposes, we benefit from three different type of information; digital elevation model (DEM) of the region which is automatically generated using stereo satellite images, possible street segment which is obtained by segmentation, and shadow information. After eliminating redundant local features, remaining features are used to detect individual persons. Those local feature coordinates are also assumed as observations of the probability density function (pdf) of the crowds to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding pdf which gives us information about dense crowd and people locations. We test our algorithm using Worldview-2 satellite images over Cairo and Munich cities. Besides, we also provide test results on airborne images for comparison of the detection accuracy. Our experimental results indicate the possible usage of the proposed approach in real-life mass events.