DGG: A Novel Framework for Crowd Gathering Detection

Abstract

Crowd gathering detection plays an important role in security supervision of public areas. Existing image-processing-based methods are not robust for complex scenes, and deep-learning-based methods for gathering detection mainly focus on the design of the network, which ignores the inner feature of the crowd gathering action. To alleviate such problems, this work proposes a novel framework Detection of Group Gathering (DGG) based on the crowd counting method using deep learning approaches and statistics to detect crowd gathering. The DGG mainly contains three parts, i.e., Detecting Candidate Frame of Gathering (DCFG), Gathering Area Detection (GAD), and Gathering Judgement (GJ). The DCFG is proposed to find the frame index in a video that has the maximum people number based on the crowd counting method. This frame means that the crowd has gathered and the specific gathering area will be detected next. The GAD detects the local area that has the maximum crowd density in a frame with a slide search box. The local area contains the inner feature of the gathering action and represents that the crowd gathering in this local area, which is denoted by grid coordinates in a video frame. Based on the detected results of the DCFG and the GAD, the GJ is proposed to analyze the statistical relationship between the local area and the global area to find the stable pattern for the crowd gathering action. Experiments based on benchmarks show that the proposed DGG has a robust representation of the gathering feature and a high detection accuracy. There is the potential that the DGG can be used in social security and smart city domains.

An Extended Model for the UAVs-Assisted Multiperiodic Crowd Tracking Problem

“…Considerable research works have been conducted in crowd management using UAVs [22,37]. Researchers have recently been using drones in crowd management to detect and track crowd movements [43][44][45]. In fact, UAVs provide powerful features such as coverage ability and fexibility over conventional static cameras previously used [40,[46][47][48][49].…”

Convolutional neural network-based crowd detection for COVID-19 social distancing protocol from unmanned aerial vehicles onboard camera

“…In crowd detection, a sensor is needed that can capture data to be translated into other forms into information on the state of the crowd. The information can be received in various forms, such as the result of human enumeration in a crowd, 7 geographical location in a density map, 8 and estimation of social distance between humans and each other 9 . Computer vision is a specific branch of science that extracts a digital image, then produces information that can be processed into several methods, such as counting methods, measuring distances, or navigation 10 .…”