Abnormal crowd event detection based on outlier in time series

Hsu, Wei-Lieh; Wang, Yucheng; Lin, Chih‐Lung

doi:10.1109/icmlc.2014.7009142

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…As the fourth sub-image in Figure 3 shows, the blue area, the A max area, contains nine smaller image patches. This area has the x-axis from [3,5] and the y-axis from [0, 2]. The crowd number in A max is 38, which is the maximum value in frame with index 150.…”

Section: The Gathering Area Detection (Gad)mentioning

confidence: 99%

“…nine smaller image patches. This area has the x-axis from [3,5] and the y-axis from [0,2]. The crowd number in 𝐴 is 38, which is the maximum value in frame with index 150.…”

Section: The Gathering Judgement (Gj)mentioning

confidence: 99%

“…Crowd gathering action has evident features compared with other human behaviors, such as quick running of crowds, crowd loitering, and crowd dispersal. Specifically, if crowd gathering events can be detected early and the relevant supervised agency can take appropriate actions towards mitigating the dangers, accidental injury can be prevented or the incident can be contained [3].…”

Section: Introductionmentioning

confidence: 99%

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DGG: A Novel Framework for Crowd Gathering Detection

Zhao

Min

et al. 2021

Electronics

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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.

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Section: The Gathering Area Detection (Gad)mentioning

confidence: 99%

“…nine smaller image patches. This area has the x-axis from [3,5] and the y-axis from [0,2]. The crowd number in 𝐴 is 38, which is the maximum value in frame with index 150.…”

Section: The Gathering Judgement (Gj)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DGG: A Novel Framework for Crowd Gathering Detection

Zhao

Min

et al. 2021

Electronics

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show abstract

“…There are no such disadvantages to unsupervised methods such as k-mean [16], Local Outlier Factor (LOF) [17], Gaussian model clusterisation [18] and histogram analysis [10], but these methods are less accurate than the supervised and semi-supervised algorithms [12]. These methods have been used to automate surveillance systems in [19], [20], [21], where the individual's behaviour is determined by the speed of movement.…”

Section: Related Workmentioning

confidence: 99%

Affective State Based Anomaly Detection in Crowd

Baliniskite

Lavendelis

Pudāne

2019

Applied Computer Systems

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To distinguish individuals with dangerous abnormal behaviours from the crowd, human characteristics (e.g., speed and direction of motion, interaction with other people), crowd characteristics (such as flow and density), space available to individuals, etc. must be considered. The paper proposes an approach that considers individual and crowd metrics to determine anomaly. An individual’s abnormal behaviour alone cannot indicate behaviour, which can be threatening toward other individuals, as this behaviour can also be triggered by positive emotions or events. To avoid individuals whose abnormal behaviour is potentially unrelated to aggression and is not environmentally dangerous, it is suggested to use emotional state of individuals. The aim of the proposed approach is to automate video surveillance systems by enabling them to automatically detect potentially dangerous situations.

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Abnormal crowd event detection based on outlier in time series

Cited by 2 publications

References 8 publications

DGG: A Novel Framework for Crowd Gathering Detection

DGG: A Novel Framework for Crowd Gathering Detection

Affective State Based Anomaly Detection in Crowd

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