Computer Vision & Embedded AI
An embedded deep learning framework for real-time violence detection and alert generation
This research introduces an optimized CNN-GRU-based violence detection framework capable of delivering high accuracy and real-time inference on embedded platforms. It bridges the gap between computationally intensive deep learning models and lightweight edge devices, achieving state-of-the-art accuracy (96.9%) with superior efficiency (26 FPS) and low power consumption (6.2 W) on a Raspberry Pi 5. The framework demonstrates robust, interpretable, and energy-efficient real-time violence detection for safety-critical environments.
Key Performance Indicators
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Accuracy
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Inference Speed
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Power Consumption
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Model Compression
Deep Analysis & Enterprise Applications
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The framework integrates a lightweight CNN for spatial feature extraction and a GRU for temporal modeling, enabling efficient spatio-temporal representation of violent actions. The CNN-GRU formulation follows established spatiotemporal modeling practices, but the primary contribution lies in the system-level design and deployment pipeline.
To ensure deployment feasibility on edge devices such as the Raspberry Pi 5, multiple optimization strategies—including quantization, structured pruning, and TensorRT acceleration—were employed. These achieve high inference speed and low energy consumption without compromising accuracy.
Evaluated on five benchmark datasets and a custom six-class dataset, the framework demonstrated an average accuracy of 96.9%, F1-score of 96.3%, and ROC-AUC of 0.972, outperforming state-of-the-art models while maintaining superior efficiency (26 FPS and 38.4 ms/frame latency).
Qualitative evaluations using Grad-CAM visualizations confirmed the model's interpretability by accurately localizing violent regions. The decision and alert module triggers automated alerts with bounding boxes and class labels when violent activity is detected, ensuring immediate situational awareness.
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Achieved Average Accuracy Across All Datasets
Embedded System Deployment Workflow
Trained PyTorch Model
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ONNX Conversion
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TensorRT Optimization (Quantization & Pruning)
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Optimized ONNX Model
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Raspberry Pi 5 Execution (ONNX Runtime)
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Real-time Inference & Alerting
| Feature | Proposed CNN-GRU | Typical Baseline (e.g., 3D-CNN, ViT) |
|---|---|---|
| Accuracy | 96.9% (high) | 92-95% (good) |
| Inference Speed | 26 FPS (real-time on RPi 5) | Often <15 FPS (on high-end GPUs) |
| Power Consumption | 6.2 W (energy-efficient) | 50W+ (high) |
| Hardware Requirement | Raspberry Pi 5 (edge device) | High-end GPUs (desktop/cloud) |
| Optimization | Quantization, Pruning, TensorRT | Limited/None for edge deployment |
| Interpretability | Grad-CAM visualization | Variable, often less focused on edge |
Real-world Scenario: Public Safety Monitoring
The framework was tested in diverse real-world scenarios, including sports arenas, streets, and public gatherings. In a scenario involving a group altercation, the system correctly identified Crowd Violence with 97.2% confidence, triggering immediate alerts. The low false alarm rate (<3%) ensures high operational reliability, making it suitable for continuous surveillance in safety-critical environments.
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Implementation Roadmap
A phased approach to integrate advanced violence detection into your existing infrastructure.
Phase 1: Architecture Design & Dataset Curation
Develop the lightweight CNN-GRU architecture and curate a comprehensive six-class dataset, along with integrating benchmark datasets. Focus on balancing class representation and data augmentation for robustness.
Phase 2: Model Training & Optimization
Train the model with Adam optimizer, cosine annealing, and early stopping. Apply quantization, structured pruning, and TensorRT acceleration to optimize for embedded deployment on Raspberry Pi 5.
Phase 3: Embedded Deployment & Real-time Validation
Deploy the optimized model on Raspberry Pi 5. Conduct real-time inference, performance benchmarking (latency, FPS, power), and qualitative evaluation with Grad-CAM and alert generation.
Phase 4: Continuous Improvement & Scalability
Explore future extensions, including multimodal sensing, federated learning, and transformer-based hybrid architectures to enhance reliability and scalability across diverse environments.
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