Enterprise AI Analysis
Intelligent Surveillance System Suspicious Activity Tracking With Yolov8 and Vision Transformer
This paper introduces a novel intelligent surveillance system that integrates YOLOv8 for high-speed object detection and Vision Transformers (ViT) for enhanced contextual understanding and visual data classification. The proposed hybrid model aims to improve real-time suspicious activity detection, addressing limitations of traditional surveillance systems and contributing to safer environments. It emphasizes ethical considerations for responsible deployment.
Quantifiable Impact
Our analysis reveals the following key performance indicators and strategic advantages:
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Average Precision (mAP)
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Real-time Detection Accuracy
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Reduction in False Positives
Deep Analysis & Enterprise Applications
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YOLOv8 excels in high-speed and precise object detection across diverse environments. Its capabilities are crucial for identifying objects and human poses with high precision in fast-paced surveillance scenarios, forming the backbone of the proposed system for initial detection and classification of suspicious behaviors like violence, non-violence, crowd discussions, and crowd fights.
Vision Transformers (ViT) leverage attention mechanisms to enhance contextual understanding and visual data classification. In this system, ViT is incorporated to handle deeper semantic relationships and temporal dependencies, providing nuanced analysis by interpreting contextual relationships between entities, distinguishing between ordinary object possession and suspicious weapon handling. This significantly enhances complex human activity recognition.
The integration of YOLOv8 and ViT creates a unified pipeline where YOLOv8 performs real-time detection and classification, while ViT refines the outputs by offering scene-level understanding. This synergy improves not only the accuracy and reliability of activity recognition but also enables the system to adapt to dynamic and cluttered environments, addressing the multifaceted challenges of modern surveillance.
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Achieved Mean Average Precision (mAP)
Enterprise Process Flow
Video Feed Capture
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YOLOv8 Object Detection
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ViT Contextual Analysis
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Suspicious Activity Classification
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Alert Generation
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Real-world Impact: Public Safety Scenario
In a crowded urban square, traditional CCTV systems often miss subtle signs of escalating conflict due to low resolution and reliance on manual review. Our hybrid YOLOv8+ViT system demonstrated its ability to detect the initial stages of a dispute, identify a concealed weapon, and classify the activity as 'suspicious weapon handling' in real-time. This early detection enabled authorities to intervene proactively, preventing potential violence and ensuring public safety. The system's contextual understanding, powered by ViT, was crucial in differentiating a normal interaction from a potential threat, showcasing a significant leap in surveillance capability.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Phased Implementation Roadmap
A structured approach ensures seamless integration and rapid value realization:
Data Preparation & Annotation
Collection and meticulous labeling of video and image data related to suspicious activities using tools like Roboflow.
Model Initialization & Pre-training
Loading pre-trained YOLOv8 (yolov8m.pt) and Vision Transformer (ViT-B/16) models on ImageNet, preparing for fine-tuning.
Hybrid Model Training & Optimization
Fine-tuning YOLOv8 and ViT on the prepared dataset, optimizing hyperparameters for real-time object detection and contextual understanding.
System Integration & Validation
Combining YOLOv8 and ViT outputs, integrating them into a unified pipeline, and validating performance with metrics like mAP, precision, and recall.
Deployment & Ethical Review
Deploying the system in target environments, ensuring scalability, responsiveness, and adhering to ethical considerations for privacy and responsible use.
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