Motion-shape-based deep learning approach for divergence behavior detection in high-density crowd

Farooq, Muhammad; Saad, Mohamad Naufal Mohamad; Khan, Sultan Daud

doi:10.1007/s00371-021-02088-4

Cited by 18 publications

(12 citation statements)

References 42 publications

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“…To reduce the gap between the synthetic data and real-world situations, they designed a Cyclic 3D Generative Adversarial Network (C3DGAN) that transforms the synthetic videos into realistic ones. Farooq et al [26] proposed the use of finite-time Lyapunov exponent (FTLE) field to represent crowddominant motion and used a CNN to detect divergence behavior in crowded scenes. Xu et al [27] proposed an approach that detects crowd escape panic behavior by using a dual-channel CNN.…”

Section: B Deep Learning-based Methodsmentioning

confidence: 99%

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

Mehmood¹

2021

IEEE Access

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Surveillance of crowded places can benefit from improved techniques of anomaly detection in crowd videos. Several existing methods have detected various types of crowd abnormal behaviors by using spatial and temporal information got from videos. So far as real-time detection of anomalies is concerned, special attention must be given to reducing the model complexity that leads to computational and memory loads. This paper proposes a low computational cost approach to detect crowd anomalies. The proposed approach avoids the expensive optical flow calculations by adopting a pre-trained 2D convolutional neural network (CNN) for motion information and implements a lighter form of the 2D CNN to achieve high recognition accuracy at low computational cost. Experiments on public datasets show that the proposed model outperforms the existing approaches in terms of detection accuracy alongside providing better performance in generating input frames.

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Section: B Deep Learning-based Methodsmentioning

confidence: 99%

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

Mehmood¹

2021

IEEE Access

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“…Despite the abundance of research conducted on motion pattern-based crowd analysis [3], [9], [10], there exists only a limited body of work specifically targeting the classification of a scene into the three distinct categories of interest: structured, semi-structured, and unstructured [7], [11]. Most existing research has explored various aspects of motion patterns without delving into the precise categorization that distinguishes between these three fundamental types of crowd behavior.…”

Section: Related Workmentioning

confidence: 99%

“…In the domain of crowd video surveillance, extensive research is currently being conducted across multiple critical dimensions. Crowd behaviour analysis [3], [4] examines the movement and interactions within crowds to improve safety. Crowd density estimation and crowd counting [5] are focused on assessing the number of people and the compactness of a crowd, which have applications in public safety and event management.…”

Section: Introductionmentioning

confidence: 99%

Motion Pattern-Based Scene Classification Using Adaptive Synthetic Oversampling and Fully Connected Deep Neural Network

Mohammed,

Al-Dhamari,

Saeed

et al. 2023

IEEE Access

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Analyzing crowded environments has become an increasingly researched topic in computer vision community, largely due to its myriad practical applications, including enhanced video surveillance systems and the estimation of crowd density in specific settings. This paper presents a comprehensive method for progressing the study of crowd dynamics and behavioral analysis, specifically focusing on the classification of movement patterns. We introduce a specialized neural network-based classifier explicitly designed for the accurate categorization of various crowd scenes. This classifier fills a unique niche in the existing literature by offering robust and adaptive classification capabilities. To optimize the performance of our model, we conduct an in-depth analysis of loss functions commonly employed in multi-class classification tasks. Our study encompasses four widely used loss functions: Focal Loss, Huber Loss, Cross-Entropy Loss, and Multi-Margin Loss. Based on empirical findings, we introduce a Joint Loss function that combines the strengths of Cross-Entropy and Multi-Margin Loss, outperforming existing methods across key performance metrics such as accuracy, precision, recall, and F1-score. Furthermore, we address the critical challenge of class imbalance in motion patterns within crowd scenes. To this end, we perform a comprehensive comparative study of two leading oversampling techniques: the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Our results indicate that ADASYN is superior at enhancing classification performance. This approach not only mitigates the issue of class imbalance but also provides robust empirical validation for our proposed method. Finally, we subject our model to a rigorous evaluation using the Collective Motion Database, facilitating a comprehensive comparison with existing state-of-the-art techniques. This evaluation confirms the effectiveness of our model and aligns it with established paradigms in the field.

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“…While numerous works have been done on motion patternbased crowd analysis [3,12,[17][18][19][20], only a few of them focus on classifying a scene into the aforementioned three categories (structured, semi-structured, and unstructured). Among them, Zhou et al [16,21] introduced a descriptor to quantify a crowded scene based on its 'collectiveness', which is defined as "the degree of individuals acting as a union in collective motion".…”

Section: Related Workmentioning

confidence: 99%

Motion pattern-based crowd scene classification using histogram of angular deviations of trajectories

et al. 2022

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Automated crowd behaviour analysis and monitoring is a challenging task due to the unpredictable nature of the crowd within a particular scene and across different scenes. The prior knowledge of the type of scene under consideration is a crucial mid-level information, which could be utilized to develop robust crowd behaviour analysis systems. In this paper, we propose an approach to automatically detect the type of a crowded scene based on the global motion patterns of the objects within the scene. Three different types of scenes whose global motion pattern characteristics vary from uniform to non-uniform are considered in this work, namely structured, semi-structured, and unstructured scenes, respectively. To capture the global motion pattern characteristics of an input crowd scene, we first extract the motion information in the form of trajectories using a key-point tracker and then compute the average angular orientation feature of each trajectory. This paper utilizes these angular features to introduce a novel feature vector, termed as Histogram of Angular Deviations (HAD), which depicts the distribution of the pair-wise angular deviation values for each trajectory vector. Since angular deviation information is resistant to changes in scene perspectives, we consider it as a key feature for distinguishing the scene types. To evaluate the effectiveness of the proposed HAD-based feature vector in classifying the crowded scenes, we build a crowd scene classification model by training the classical machine learning algorithms on the publicly available Collective Motion Database. The experimental results demonstrate the superior crowd classification performance of the proposed approach as compared to the existing methods. In addition to this, we propose a technique based on quantizing the angular deviation values to reduce the feature dimension and subsequently introduce a novel crowd scene structuredness index to quantify the structuredness of an input crowded scene based on its HAD.

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Motion-shape-based deep learning approach for divergence behavior detection in high-density crowd

Cited by 18 publications

References 42 publications

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

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

Motion Pattern-Based Scene Classification Using Adaptive Synthetic Oversampling and Fully Connected Deep Neural Network

Motion pattern-based crowd scene classification using histogram of angular deviations of trajectories

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