Intelligent Crowd Analysis: A Learning-Based Approach for Detection and Behavior Analysis in Surveillance Videos

Traditional ML-based analysis of anomalous crowd behavior: A thorough review

Amneen: An AI-Based System for Real-Time Tracking and Management of Crowds

Amneen is a crowd management system that uses Artificial Intelligence (AI) and Computer Vision (CV) to track crowd density in public places and prevent dangerous situations before they occur, and predicts congestion before it occurs.

Lightweight recognition network for dense crowd images based on YOLO11

In response to the challenges faced by autonomous driving and crowd flow monitoring systems in dense occlusion crowd detection, as well as the low accuracy and high computational cost of other detection algorithms, an improved lightweight dense crowd detection model named MGN-YOLO based on YOLO11 is proposed. The model adopts MobileViT as its backbone network, which effectively enhances the overall feature extraction capability of the model in dense crowd scenarios. To encode global information, the GMA attention mechanism module is incorporated, which further aggregates pixel-level features through dimensional interaction and improves small-object detection by integrating a 160×160-scale detection head. By employing CIoU-NWD as the bounding box loss function, the issues of missed detection and false detection for small-scale crowded objects in dense scenarios are effectively alleviated. The experimental results show that compared with YOLO11n, the accuracy of the MGN-YOLO crowd detection algorithm is improved by 4.4%(mAP_0.5/%) on the CrowdHuman and 2.1% on the WiderPerson, while only 2.7M parameters and 7.7 GFLOPs of model calculations, which meet the deployment requirements of low computing power and high precision.

Artificial Intelligence Guided Crowd Counting and Density Estimation with Enhanced CSRNet

Estimating crowd size in dense environments re-mains a complex problem, yet it holds critical value for safety monitoring, city infrastructure design, and large-scale gathering coordination. Leveraging contemporary developments in neural network architectures and machine intelligence, researchers have markedly enhanced the precision of population counts derived from both still imagery and motion footage. This research focuses on the development of an enhanced deep neural network-based crowd counting model. Since the original CSRNet primarily relies on head detection for crowd estimation, an additional face detection module has been incorporated to improve its capability in scenarios where facial features are visible. The proposed enhancement increases the flexibility and estimation accuracy of CSRNet by integrating individual face detection with crowd density estimation. Furthermore, this study presents a comprehensive comparative analysis of the proposed enhanced CSRNet against three state-of-the-art crowd counting models, namely Bayesian Network (BAYNet), Distribution Matching (DM-Count), and Scale Aggregation Feature Attention Network (SFANet). The models are evaluated on multiple datasets to investigate their accuracy, robustness, adaptability to varying crowd densities, and performance under complex environmental conditions. The comparison also highlights the methodological differences and computational characteristics of these approaches. Model performance is assessed using the standard evaluation metrics of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Experimental results demonstrate that the proposed enhanced CSRNet achieves a competitive RMSE/MAE of 9.61/93.05 on 64 × 64 images of the custom dataset, outperforming the baseline models and demonstrating its effectiveness for accurate crowd counting.

A Hybrid Intelligent Crowd Monitoring System for Real-Time Public Safety

A hybrid intelligent crowd monitoring system comprising YOLOv8-based person detection, crowd density estimation, crowd flow analysis, abnormal crowd behavior detection, and alert generation is introduced.

Pedestrian Detection in Crowded Environments Using a Hybrid Deep Learning Model with Occlusion-Aware Attention

Pedestrian detection in crowded environments is an important computer vision problem, which has been broadly applied to intelligent transportation systems, autonomous vehicle, smart surveillance and public safety. Yet, accurate pedestrian detection in dense scenes is still difficult because of the significant occlusion, overlap, scale variation of objects and complex background. While the accuracy of detection has been greatly enhanced by deep learning methods, current models are not able to detect partly visible pedestrians, resulting in missed detections and false positives. A hybrid deep learning model that combines the benefits of both SSI and CNN by incorporating an Occlusion-Aware Attention (OA) mechanism for enhancing pedestrian detection in dense scenarios is proposed. The proposed framework combines a Swin Transformer backbone for feature extraction, a Feature Pyramid Network (FPN) for multi-scale feature learning, and a customized Occlusion-Aware Attention module to enhance the detection of partially occluded pedestrians. To detect overlapping pedestrians efficiently, a YOLO based detection head is used and Soft Non-Maximum Suppression (Soft-NMS) is adopted to refine the detected overlapping pedestrians. The framework will be tested with the benchmark datasets: CrowdHuman, CityPersons and WiderPerson, and the metrics used will be: Precision, Recall, F1-Score, mAP@0.5 and inference speed. The proposed model is expected to achieve high accuracy in detection, low probability of missed detection in high occlusion rate and efficient solution for real-time pedestrian detection in complex urban environment.