Detecting anomalous crowd behavior using correlation analysis of optical flow

Nayan, Navneet; Sahu, Sitanshu Sekhar; Kumar, S. Pravin

doi:10.1007/s11760-019-01474-9

Cited by 19 publications

(5 citation statements)

References 29 publications

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“…The threshold value is considered to make decisions for anomalous event detection. Further, the proposed methodology has been implemented on all 11 videos of the UMN dataset [30,31].…”

Section: Results Analysismentioning

confidence: 99%

“…Detection has been done by using SVM classification [28,29]. Again, by considering correlation between two consecutive frames optical flow magnitude [30], div-curl [31], entropy [32] characteristics give good accuracy to detect anomalies in crowded regions. Further to detect and locate anomalies presented in video [33] proposed a novel work by considering a concept of momentum from physics.…”

Section: Related Workmentioning

confidence: 99%

“…Here in our experiment, we have considered Horn and Schunk's approach. We begin with the assumption of brightness constancy [30] eq.2 for an intensity f(i,j,t) and enlarge this in the Taylor series format as follows:…”

Section: Optical Flow Analysismentioning

confidence: 99%

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Crowd behavior anomaly detection using correlation of optical flow magnitude

Chakole

Satpute

Cheggoju

2022

J. Phys.: Conf. Ser.

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Nowadays, crowd monitoring is a contentious issue. Because of the increasing population and diversity of human activities, crowd scenarios in the real world are becoming more common, demanding the need for an automotive anomaly detection system. Crowd behavior is influenced by the thoughts and attitudes of others around them. An unexpected event can turn a peaceful crowd into a riot. A mechanism based on optical flow must be implemented to compensate for all of these factors. The amount of motion present in two successive frames is estimated using optical flow. It includes information on velocity in the x & y plane, along with magnitude and line of action. By means of “anomalous event” in this paper is quick and sudden dispersal of the crowd. For detecting an event the magnitude of two successive frames should be taken into account followed by estimating a correlation. We expect a high correlation, slight motion, and low rate of change in velocities at non-anomalous events, but as soon as an anomalous event occurs, the correlation begins to decrease with a significant change in velocity and large motion vectors. The methodology was tested on a dataset from the University of Minnesota that included 11 movies from three different circumstances. Almost all anomalous occurrences in videos were successfully detected using this method.

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“…The threshold value is considered to make decisions for anomalous event detection. Further, the proposed methodology has been implemented on all 11 videos of the UMN dataset [30,31].…”

Section: Results Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Crowd behavior anomaly detection using correlation of optical flow magnitude

Chakole

Satpute

Cheggoju

2022

J. Phys.: Conf. Ser.

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“…Computer vision algorithms that utilise image processing, machine learning and pattern recognition depict the challenges in crowd behavior analysis [5][6][7]. Some of its most crucial applications are crowd control, video surveillance, and the design of intelligent public spaces [8][9][10][11][12][13][14]. The intelligent environment, which is essential for public safety, can help to redirect the crowd and help the planner to design the public area with the most available space [4].…”

Section: Crowd Anomaly Detection (Cad)mentioning

confidence: 99%

A Review of the Recent Progress on Crowd Anomaly Detection

Altowairqi¹,

Luo²,

Greer³

2023

IJACSA

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Surveillance videos are crucial in imparting public security, reducing or avoiding the accidents that occur from anomalies. Crowd anomaly detection is a rapidly growing research field that aims to identify abnormal or suspicious behavior in crowds. This paper provides a comprehensive review of the state-of-the-art in crowd anomaly detection and, different taxonomies, publicly available datasets, challenges, and future research directions. The paper first provides an overview of the field and the importance of crowd anomaly detection in various applications such as public safety, transportation, and surveillance. Secondly, it presents the components of crowd anomaly detection and its different taxonomies based on the availability of labels, and the type of anomalies. Thirdly, it presents the review of the recent progress of crowd anomaly detection. The review also covers publicly available datasets commonly used for evaluating crowd anomaly detection methods. The challenges faced by the field, such as handling variability in crowd behavior, dealing with large and complex data sets, and addressing the imbalance of data, are discussed. Finally, the paper concludes with a discussion of future research directions in crowd anomaly detection, including integrating multiple modalities, addressing privacy concerns, and addressing crowd monitoring systems' ethical and legal implications.

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“…Regarding the study of groups of people, advances in analysing behaviour are limited to very concrete and simple activities or actions, usually of short duration (low semantic component) such as a actions in sport games [13], [37], [42], [53], detection interactions of people inside a group [15], [57], [60], inter-group violence [51], [64], [65], among others. If we increase the number of people in the group, becoming crowds, the level of semantics is even lower, being specifically limited to tasks such as counting people and calculating crowd density [8], [18], [25], [68] or detecting movements of a mass of people or crowd collisions [21], [39], [49], [71], mainly for the purpose of security tasks. It is important to highlight the work in [54], in which the authors present a model of learning based on contextual relationships that uses a deep neural network to recognize activities in a video sequence.…”

Section: Related Workmentioning

confidence: 99%

Architecture for Automatic Recognition of Group Activities Using Local Motions and Context

et al. 2022

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Currently, the ability to automatically detect human behavior in image sequences is one of the most important challenges in the area of computer vision. Within this broad field of knowledge, the recognition of activities of people groups in public areas is receiving special attention due to its importance in many aspects including safety and security. This paper proposes a generic computer vision architecture with the ability to learn and recognize different group activities using mainly the local group's movements. Specifically, a multi-stream deep learning architecture is proposed whose two main streams correspond to a representation based on a descriptor capable of representing the trajectory information of a sequence of images as a collection of local movements that occur in specific regions of the scene. Additional information (e.g. location, time, etc.) to strengthen the classification of activities by including it as additional streams. The proposed architecture is capable of classifying in a robust way different activities of a group as well to deal with the one-class problems. Moreover, the use of a simple descriptor that transforms a sequence of color images into a sequence of two-image streams can reduce the curse of dimensionality using a deep learning approach. The generic deep learning architecture has been evaluated with different datasets outperforming the state-of-the-art approaches providing an efficient architecture for single and multi-class classification problems.

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Detecting anomalous crowd behavior using correlation analysis of optical flow

Cited by 19 publications

References 29 publications

Crowd behavior anomaly detection using correlation of optical flow magnitude

Crowd behavior anomaly detection using correlation of optical flow magnitude

A Review of the Recent Progress on Crowd Anomaly Detection

Architecture for Automatic Recognition of Group Activities Using Local Motions and Context

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