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.
Highlights
The development of surveillance systems is an important research topic in the computer field
Based on the detected results of the Detecting Candidate Frame of Gathering (DCFG) and the Gathering Area Detection (GAD), the Gathering Judgement (GJ) is proposed to analyze the statistical relationship between the local area and the global area to find the stable pattern for crowd gathering detection
This work proposes a novel framework Detection of Group Gathering (DGG) based on crowd counting methods using deep learning approaches and statistics to detect crowd gathering
Summary
The development of surveillance systems is an important research topic in the computer field. It is necessary to develop an automatic monitoring program suitable for various applications such as fault detection and event detection in sensors to detect patterns that do not conform to the established normal behavior. One of the most interesting and active research topics in computer vision is the analysis of crowd behavior [1]. Crowd gathering belongs to such abnormal action. Crowd gathering detection aims to detect the crowd gathering actions in videos that always happen in public spaces [2]. When people appearing in an area, they may be scattered or gathered together. The crowd gathering action is dangerous in a crowded place as it may probably cause people to fall or may inflict injuries
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