Multi-Scale Network with Integrated Attention Unit for Crowd Counting

Hafeezallah, Adel; Al-Dhamari, Ahlam; Abu-Bakar, S. A. R.

doi:10.32604/cmc.2022.028289

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…A weekly-supervised transformer was used for crowd counting with adaptive scene consistency attention [20]. A multi-scale attention network was devised for crowd counting [21]. An attentionbased crowd counting model was developed to alleviate the problem of uneven distribution of crowd density [22].…”

Section: Literature Surveymentioning

confidence: 99%

Training a Regression-Based Model for Crowd Counting in Transit Cars Using Ranked Image Pairs and Triplets

Lee,

Kang

et al. 2024

IEEE Access

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Accurately measuring the level of crowding in transit cars is crucial for ensuring passenger safety and efficient operation. However, applying object detection algorithms to crowd co unting in transit cars poses difficulties due to the low viewpoint of the cameras and the labor-intensive task of image labeling. Although some researchers have explored regression-based crowd counting methods without labeling with bounding boxes, their approaches still necessitate manual counting of passengers for image labeling. To overcome these challenges, we propose a novel calibration method for regression -based models that minimizes the number of labeled images required for training. Our approach emp loys image pairs and triplets with ranks for reinforcing the model training. Subsequently, the training task requires a minimal number of images labeled with exact passenger counts. Experimental results demonstrate that our proposed calibration approach considerably enhances the crowd counting performance of the conventional regression -based model. Specifically, our method reduces the mean absolute error (MAE) by 76.5% and 34.3% for conventional detection-and regression-based calibration methods, respectively. INDEX TERMSPassenger load in transit, Crowd counting, Computer vision, Regression -based model, Ranking model

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Section: Literature Surveymentioning

confidence: 99%

Training a Regression-Based Model for Crowd Counting in Transit Cars Using Ranked Image Pairs and Triplets

Lee,

Kang

et al. 2024

IEEE Access

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“…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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Motion segmentation using Ward’s hierarchical agglomerative clustering for crowd disaster risk mitigation

Hafeezallah,

Al-Dhamari,

Abu-Bakar

2024

International Journal of Disaster Risk Reduction

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Multi-Scale Network with Integrated Attention Unit for Crowd Counting

Cited by 4 publications

References 27 publications

Training a Regression-Based Model for Crowd Counting in Transit Cars Using Ranked Image Pairs and Triplets

Training a Regression-Based Model for Crowd Counting in Transit Cars Using Ranked Image Pairs and Triplets

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

Motion segmentation using Ward’s hierarchical agglomerative clustering for crowd disaster risk mitigation

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