Enterprise AI Analysis
Real-time abnormal behaviour detection using energy-efficient YOLO-based framework
This paper introduces a novel approach to detect and analyse abnormal behaviour in video data by leveraging an optimised YOLO network. The proposed system consists of multiple interconnected modules that work together seamlessly, resulting in exceptional accuracy for both detection and analysis tasks. The system demonstrates efficiency and accuracy in abnormal behaviour detection by employing a CNN with Adams optimisation, histogram equalisation using the CDF method, EuclideanDistTracker for object tracking, confidence threshold detection, post-processing techniques, non-max suppression, edge-based segmentation using the Prewitt operator, and human detection using an optimised YOLO framework. The model has archived an accuracy of 0.99%. The performance analysis of the proposed system yields promising results, indicating its suitability for a wide range of real-time applications such as surveillance systems, traffic monitoring, and public safety systems. Furthermore, with continuous advances in computer vision and deep learning methods, the suggested approach has the potential to be further optimised for even better accuracy in recognising and analysing abnoraml behaviour.
Executive Impact: Key Performance Indicators
Our analysis of the research reveals significant improvements across critical enterprise metrics, translating directly to enhanced operational efficiency and security. The Optimized YOLO framework demonstrates superior capabilities in abnormal behavior detection.
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Accuracy
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PSNR (Optimized YOLOv4)
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F1-Score (Full Model)
Deep Analysis & Enterprise Applications
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The proposed model offers a complete approach for detecting and analysing abnormal behaviour using an optimised YOLO network, enhanced through Adam Optimization, histogram equalisation, and other refinement techniques. For temporal analysis, it employs the Euclidean Distance Tracker, while detection quality is improved through post-processing methods such as confidence thresholding and non-max suppression. Additionally, edge-based segmentation using the Prewitt operator and an enhanced YOLO framework are applied for accurate human detection.
Optimization is crucial for enhancing detection performance. The system employs Adam Optimization, which dynamically adjusts learning rates and leads to faster convergence and more stable parameter updates. Histogram Equalization (CDF method) enhances contrast and highlights subtle motion variations, improving feature discriminability for the CNN and Optimized YOLO framework.
The model provides a remarkable accuracy of 99.46% in identifying and analysing abnormal behaviour, making it applicable to various real-time applications such as surveillance, traffic management, and public safety. Its real-time performance and accuracy are critical for early detection of potential threats and safeguarding vulnerable populations.
99.46%
Overall Accuracy Achieved
Enterprise Process Flow
Input Real Time Video
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Converting frames
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Training & Testing (CNN + Adam)
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Histogram equalization (CDF)
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Euclidean Dist Tracker
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Confidence Threshold
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Post Processed Objects
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Non-Max Suppression
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Edge based Segmentation (Prewitt)
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Human Detection (Optimised YOLO)
Performance Metrics Comparison (Table 6)
| Performance Metrics | CNN | YOLOv4 | Optimised YOLOv4 |
|---|---|---|---|
| Accuracy | 0.7845 | 0.9816 | 0.9946 |
| Precision | 0.8489 | 0.9816 | 0.9924 |
| Recall | 0.8457 | 0.9826 | 0.9879 |
| F1 score | 0.8496 | 0.9836 | 0.9926 |
| Test accuracy | 0.7658 | 0.9816 | 0.9926 |
Impact in Public Safety & Surveillance
The ability to accurately detect abnormal behavior in real-time offers significant advantages for public safety. For instance, in urban surveillance networks, the system can identify unusual pedestrian movements (e.g., running in restricted areas) or unattended objects, triggering early alerts. This proactive approach significantly reduces response times for security personnel. Compared to traditional systems that rely on manual monitoring or less efficient algorithms, our Optimized YOLO framework provides a superior detection rate and minimizes false positives, ensuring that resources are deployed effectively. The integration of Adam Optimization and Histogram Equalization further refines the detection, making the system robust against varying environmental conditions and lighting changes.
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Implementation Roadmap
Our structured approach ensures a smooth integration and maximizes the impact of your AI investment. Here's a typical timeline for deploying the abnormal behavior detection system.
Phase 1: Initial System Setup & Data Integration
Deployment of the core Optimized YOLO framework. Integration with existing surveillance infrastructure and initial data feeds. Baseline model training on specific organizational datasets.
Phase 2: Customization & Refinement
Fine-tuning of detection parameters based on specific environmental factors and desired anomaly types. Implementation of Adam Optimization and Histogram Equalization for enhanced accuracy. Integration of Euclidean Distance Tracker for object tracking.
Phase 3: Real-time Deployment & Monitoring
Full deployment for real-time analysis. Continuous monitoring and evaluation of system performance. Iterative improvements based on operational feedback and identified abnormal patterns.
Phase 4: Scalability & Expansion
Expansion to additional surveillance zones or integration with broader enterprise security systems. Exploration of new abnormal behavior categories and advanced analytics modules for predictive capabilities.
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