Computer Vision, Machine Learning, Deep Learning
Robust crowd anomaly detection via hybrid ensemble learning for real-world surveillance
This research introduces a novel hybrid ensemble learning framework for crowd anomaly detection (CAD) in real-time surveillance. It integrates YOLOv7 for efficient crowd detection with Random Forests (RFs) and Gradient Boosting (GB) classifiers for robust behavior classification. The model extracts spatial and motion features via optical flow, reduces dimensionality, and is optimized with Adam for small-scale datasets. Achieving 99.89% accuracy on UMN and strong generalizability on a custom supermarket dataset, it sets a new standard for CAD in challenging, real-world scenarios, improving public safety and surveillance efficiency.
Executive Impact
This robust hybrid AI model significantly enhances real-time crowd anomaly detection, delivering unparalleled accuracy on small-scale datasets and ensuring practical applicability for critical surveillance systems. Its generalizability across diverse environments directly translates to improved public safety and operational efficiency for enterprises, reducing risks and resource drain.
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UMN Dataset Accuracy
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Supermarket Dataset Accuracy
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Real-time Processing (UMN)
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Real-time Processing (Supermarket)
Deep Analysis & Enterprise Applications
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Methodology
Performance
Practicality
The proposed framework integrates YOLOv7 for real-time crowd region detection, optical flow for motion dynamics, and a hybrid ensemble of Random Forests (RFs) and Gradient Boosting (GB) for classification. This combination balances computational efficiency, accuracy, and interpretability, making it suitable for diverse surveillance environments.
Achieved 99.89% accuracy on the UMN dataset and 92.6% on a custom supermarket dataset. The model demonstrates strong generalizability and robustness, particularly on small-scale datasets, surpassing previous state-of-the-art methods in benchmark comparisons.
The system processes frames at 24 FPS (UMN) and 15.6 FPS (supermarket), meeting real-time surveillance requirements. Its ability to handle varying lighting, occlusions, and crowd densities makes it highly applicable for real-world scenarios like retail security and public safety, enhancing proactive threat detection.
99.89%
Near-perfect accuracy on UMN benchmark dataset for crowd anomaly detection.
Enterprise Process Flow
Input Video Frames
→
YOLOv7 (Crowd Detection)
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Optical Flow (Motion Dynamics)
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Feature Extraction (Spatial & Temporal)
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Dimensionality Reduction
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Hybrid Ensemble (RF + GB)
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Normal / Abnormal Classification
| Method | Accuracy (%) (Scene 1) | Accuracy (%) (Scene 2) | Accuracy (%) (Scene 3) | Average Accuracy (%) |
|---|---|---|---|---|
| CNNs and RFs | 99.7 | 99.7 | ||
| CNN Residual L.S.T.M. | 98.2 | 98.2 | ||
| Optical Flow GAN | 99.4 | 97.1 | 97.6 | 99.89 (overall reported in paper, but scene specific values from paper table are used here for individual scenes, original paper table average seems miscalculated or missing entries) |
| Proposed Method | 99.85 | 99.89 | 99.88 | 99.89 |
| Note: Some methods did not report scene-specific accuracies, leading to null values for individual scenes. The average accuracy for Optical Flow GAN is 99.89% as stated in the paper's comparison table overall accuracy column, though individual scene accuracy for this method are from the figure in the paper. | ||||
Supermarket Surveillance Enhancement
The proposed hybrid anomaly detection system was rigorously tested on a custom supermarket dataset, demonstrating its effectiveness in a complex real-world environment. It successfully detected diverse anomalies, including shoplifting, loitering, and suspicious object handling, with an overall accuracy of 92.6%. This capability significantly enhances retail security, enabling proactive interventions and reducing potential losses. The model’s robustness against varying lighting, occlusions, and crowd densities, typical of indoor retail settings, proves its practical value beyond traditional benchmarks.
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Your AI Implementation Roadmap
Integrating robust AI solutions requires a clear strategy. Our proven roadmap ensures a seamless transition and maximum impact for your enterprise.
Discovery & Strategy
We begin with a deep dive into your current surveillance infrastructure, operational challenges, and business objectives. This phase defines the scope, identifies key anomaly types, and customizes the AI model to your specific environment.
Data Preparation & Model Training
Leveraging your existing video data and, if necessary, generating synthetic data, we prepare a high-quality dataset. Our hybrid ensemble model is then trained and fine-tuned, utilizing advanced optimization techniques like Adam to ensure superior accuracy and generalization.
Integration & Deployment
The trained AI model is seamlessly integrated into your existing surveillance systems. This includes API development for real-time alerts, dashboard creation for monitoring, and ensuring compatibility with your hardware (e.g., edge devices for real-time processing).
Monitoring & Optimization
Post-deployment, we provide continuous monitoring and performance analysis. This iterative process allows for ongoing model refinement, adaptation to new anomaly patterns, and optimization of system efficiency to maintain peak operational effectiveness.
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