Social network model for crowd anomaly detection and localization

Chaker, Rima; Aghbari, Zaher Al; Junejo, Imran N.

doi:10.1016/j.patcog.2016.06.016

Cited by 113 publications

(57 citation statements)

References 29 publications

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“…PR is a scientific area of study dedicated to analyze patterns and regularities in data [3]. PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54].…”

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confidence: 99%

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

et al. 2019

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The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings. Kittler et al. in [13] proposed a taxonomy of anomalies which expanded the concept of anomaly beyond the conventional meaning of outlier. They used sensory data quality assessment [26], contextual [9] and non-contextual [27][28][29][30] classifiers [5,[15][16][17][18], and an incongruence indicator [16][17][18] to identify each type of anomaly [13]. According to this taxonomy [13] anomalies can be, for example, of the types unknown object, measurement model drift, unknown structure, unexpected structural component, component model drift, and unexpected structure and structural components. This taxonomy [13] is well-known and widely accepted by the scientific community because it has the potential to be applied for solving problems in many different research areas [13]. Therefore, studies related to the application of the taxonomy [13] on synthetic data are common, such as in [16][17][18]. However, to the knowledge of the authors, studies have not addressed the practical application of the taxonomy [13] to solve real-world problems [2,[31][32][33], because it remains a challenge for all research areas.Anomaly detection [13] and incongruence [15][16][17][18] are two powerful computational tools from pattern recognition (PR) [3,[11][12][13][14][15][16][17][18]34,35] and computer vision (CV) [3,36]. PR is a scientific area of study dedicated to analyze patterns and regularities in data [3]. PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54]. Additionally, PR also provides powerful tools to help machine perception...

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confidence: 99%

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

et al. 2019

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“…The comparing methods include Sparse, 14 Adam, 12 MPPCA+SF, 33 SF, 33 MPPCA, 33 MDT, 33 STMC 36 and SNM. 37 In the pixel level criterion, our proposed method obtains the best results both in RD and AUC criteria. Most of our detected pixels are in the ground truth mask, thus in the frame level criterion, our proposed method does not obtain the best performance.…”

Section: B Lae Detectionmentioning

confidence: 89%

“…Fusing the current descriptor with the object detection method for action recognition in surveillance videos is also in the prospective research scopes. 12 38 % 24 % 13.3 % MPPCA+SF 33 32 % 27 % 21.3 % SF 33 31 % 21 % 17.9 % MPPCA 33 40 % 18 % 20.5 % MDT 33 25 % 45 % 44.1 % STMC 36 23 % 47 % 47.1 % SNM 37 -48.5 % -CS (ours) 23 % 57 % 61.0 %…”

Section: B Lae Detectionmentioning

confidence: 99%

Abnormal global and local event detection in compressive sensing domain

et al. 2018

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Abnormal event detection, also known as anomaly detection, is one challenging task in security video surveillance. It is important to develop effective and robust movement representation models for global and local abnormal event detection to fight against factors such as occlusion and illumination change. In this paper, a new algorithm is proposed. It can locate the abnormal events on one frame, and detect the global abnormal frame. The proposed algorithm employs a sparse measurement matrix designed to represent the movement feature based on optical flow efficiently. Then, the abnormal detection mission is constructed as a one-class classification task via merely learning from the training normal samples. Experiments demonstrate that our algorithm performs well on the benchmark abnormal detection datasets against state-of-the-art methods.

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“…However, traditional surveillance algorithms may break down as they are unable to process high density crowds due to limitations in their design. In such scenarios, we can leverage the results of algorithms specially designed for crowd analysis related tasks such as behavior analysis [83,48], congestion analysis [114,40], anomaly detection [56,14] and event detection [8]. Disaster management: Many scenarios involving crowd gatherings such as sports events, music concerts, public demonstrations and political rallies face the risk of crowd related disasters such as stampedes which can be life threatening.…”

Section: Introductionmentioning

confidence: 99%

A survey of recent advances in CNN-based single image crowd counting and density estimation

Sindagi¹,

Patel²

2018

Pattern Recognition Letters

502

243

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Estimating count and density maps from crowd images has a wide range of applications such as video surveillance, traffic monitoring, public safety and urban planning. In addition, techniques developed for crowd counting can be applied to related tasks in other fields of study such as cell microscopy, vehicle counting and environmental survey. The task of crowd counting and density map estimation is riddled with many challenges such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Nevertheless, over the last few years, crowd count analysis has evolved from earlier methods that are often limited to small variations in crowd density and scales to the current state-of-the-art methods that have developed the ability to perform successfully on a wide range of scenarios. The success of crowd counting methods in the recent years can be largely attributed to deep learning and publications of challenging datasets. In this paper, we provide a comprehensive survey of recent Convolutional Neural Network (CNN) based approaches that have demonstrated significant improvements over earlier methods that rely largely on hand-crafted representations. First, we briefly review the pioneering methods that use hand-crafted representations and then we delve in detail into the deep learning-based approaches and recently published datasets. Furthermore, we discuss the merits and drawbacks of existing CNN-based approaches and identify promising avenues of research in this rapidly evolving field.

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Social network model for crowd anomaly detection and localization

Cited by 113 publications

References 29 publications

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

Abnormal global and local event detection in compressive sensing domain

A survey of recent advances in CNN-based single image crowd counting and density estimation

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