Efficient Anomaly Detection in Crowd Videos Using Pre-Trained 2D Convolutional Neural Networks

Mehmood, Abid

doi:10.1109/access.2021.3118009

Cited by 35 publications

(14 citation statements)

References 48 publications

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“…The benchmarked dataset that is used for the testing of this model's performance is CUHK. Andit introduces a methodology based on data mining used the analysis the behaviour of the crowd in crowded conditions based on WIFI sensing [12]. In addition to this, a detailed and specific data analysis is performed for the collection of probe requests and estimation of object patterns.…”

Section: Related Workmentioning

confidence: 99%

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An integrated multi-level feature fusion framework for crowd behaviour prediction and analysis

Murthygowda¹,

Krishnegowda²,

Shashikala³

2023

IJEECS

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<p>The uncontrolled outburst in population has led to crowd gatherings in various public places causing panic and disaster in certain unpleasant and extreme conditions. A study on the analysis of crowd accumulation has been carried out for various reasons that include management of crowd, design of a well-planned public space, the possibility for surveillance at every area and transportation systems. A lot of disasters also occurs due to uncontrollable crowd behaviour and poor crowd management. It could result in loss of property, fatalities or casualties. To avoid this, the conduct of a crowd of people has been studied and analyzed to control their movement and traffic. Hence, in this research work, integrated multi-level feature fusion (IMFF) framework is designed to predict the behaviour; further classification based on the local region is carried out to enhance the prediction. In the case of multi-level feature fusion; first level feature fusion utilizes the motion and appearance; second-level feature fusion utilizes the spatial connection and third-level utilizes the temporal connections. Further, the classification approach is integrated based on the local region is used to enhance the crowd behaviour prediction in terms of accuracy and faster. Moreover, performance evaluation is carried out considering the two distinctive datasets.</p>

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Section: Related Workmentioning

confidence: 99%

“…The weight of the feature size should be considered small. Therefore, the function of the object is defined by (12).…”

Section: Second Level Feature Fusionmentioning

confidence: 99%

An integrated multi-level feature fusion framework for crowd behaviour prediction and analysis

Murthygowda¹,

Krishnegowda²,

Shashikala³

2023

IJEECS

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“…Violations are found utilizing likelihood ratio analysis and categories of pedestrian legal action in an unsupervised learning framework. The author of [38] described a technique for finding abnormalities in a spatio-temporal environment. In a scene that comprises the object's position, movement, direction, and velocity, they displayed an atomic event for a single object.…”

Section: Suspicious Activity Detectionmentioning

confidence: 99%

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Gawande¹,

Hajari²,

Golhar

2023

Preprint

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Pedestrian detection and suspicious activity recognition are notable challenges in vision-based surveillance systems. However, the accuracy of pedestrian detection is influenced by a wide range of factors, including human presence, trajectory, posture, complex background, and object deformation. In this paper, we developed a pedestrian dataset that encompasses student behavior in an institution, such as cheating, stealing lab devices, dispute, and threatening scenarios. It provides uniform and steady identification annotations of pedestrians, making it suitable for pedestrian detection, tracking, and behavior detection. In addition, we also improved detection accuracy using the enhanced YOLOv5 architecture. Next, we proposed an efficient method for detection of global and local unusual behaviors. The proposed method extracts motion features that accurately describe pedestrian motion, speed, and direction, etc. The proposed method is validated on a proposed student behavior database and three additional publicly available benchmark datasets. It gives state-of-the-art detection accuracy of 96.12% and miss rate of 6.68% compared to the existing techniques. The experimental results demonstrate a substantial performance improvement in anomalous activity recognition. The final section summarizes and discusses future research directions.

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“…In an unsupervised learning framework, violations are discovered using likelihood ratio analysis and pedestrian legal action categories. Mehmood [18] presented a method for detecting anomalies in a spatiotemporal environment. They showed an atomic event for a single object in a scene that includes the object's position, movement, direction, and velocity.…”

Section: Literature Surveymentioning

confidence: 99%

Novel approach for pedestrian unusual activity detection in academic environment

Hajari

Gawande

Golhar

2022

IJ-AI

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<span>In this paper, we propose an efficient method for the detection of student unusual activity in the academic environment. The proposed method extracts motion features that accurately describe the motion characteristics of the pedestrian's movement, velocity, and direction, as well as their intercommunication within a frame. We also use these motion features to detect both global and local anomalous behaviors within the frame. The proposed approach is validated on a newly built proposed student behavior database and three additional publicly available benchmark datasets. When compared to state-of-the-art techniques, the experimental results reveal a considerable performance improvement in anomalous activity recognition. Finally, we summarize and discuss future research directions.</span>

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Efficient Anomaly Detection in Crowd Videos Using Pre-Trained 2D Convolutional Neural Networks

Cited by 35 publications

References 48 publications

An integrated multi-level feature fusion framework for crowd behaviour prediction and analysis

An integrated multi-level feature fusion framework for crowd behaviour prediction and analysis

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Novel approach for pedestrian unusual activity detection in academic environment

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