You will get a Intrusion Detection System
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Project details
The Intrusion Detection is an AI-driven surveillance system that is designed to monitor camera feeds, detect unauthorized entries. It provides real-time alerts, manages the health and status of cameras and devices. The system is designed for reliability, scalability, and efficient handling of multiple monitoring scenarios.
Key Features:
1. Person Detection: Uses YOLO-based models (ONNX) for real-time detection of people in live streams.
2. ROI Management: Allows configuration and management of specific regions within camera views to monitor for intrusions.
3. Camera and Device Management: Handles multiple cameras and devices, including their configuration, status monitoring, and heartbeat signals.
4. Messaging Integration: Uses RabbitMQ for inter-process and inter-service communication, including sending intrusion events and receiving commands.
Key Features:
1. Person Detection: Uses YOLO-based models (ONNX) for real-time detection of people in live streams.
2. ROI Management: Allows configuration and management of specific regions within camera views to monitor for intrusions.
3. Camera and Device Management: Handles multiple cameras and devices, including their configuration, status monitoring, and heartbeat signals.
4. Messaging Integration: Uses RabbitMQ for inter-process and inter-service communication, including sending intrusion events and receiving commands.
AI Development Type
Deep Learning, Model TuningAI Tools
Azure Machine Learning, Keras, MATLAB, MLflow, NVIDIA AI Platform, PyTorch, TensorFlowAI Development Language
PythonWhat's included $8,000
These options are included with the project scope.
$8,000
- Delivery Time 14 days
- Number of Revisions 1
- AI Model Integration
- Detailed Code Comments
- Knowledge Graph
- Model Documentation
- Ontology
- Source Code
- Taxonomy
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About Ruchir
Computer Vision Engineer | AI/ML & Deep Learning Specialist
100%
Job Success
Ahmedabad, India - 3:18 pm local time
My core focus is on computer vision, especially tasks involving image understanding, detection, and extraction from complex or noisy inputs.
I have strong experience working with:
✔️Image Classification & Fine-Grained Recognition (handling subtle visual differences)
✔️Object Detection (YOLO, SSD, Faster R-CNN, TFOD API)
✔️Image Segmentation (Mask R-CNN, semantic & instance segmentation)
✔️OCR & Text Extraction (structured documents, multi-format, noisy images)
✔️Image Preprocessing (denoising, deskewing, perspective correction, enhancement)
✔️OpenCV-based pipelines for real-time and production use
✔️Deep Learning frameworks: TensorFlow, Keras, PyTorch
✔️CNN Architectures: ResNet, VGG, Inception, EfficientNet
✔️Transfer Learning & Custom Model Training
✔️Synthetic Data Generation & Augmentation
✔️Vector Embeddings & Image Similarity Systems
✔️End-to-End CV Pipelines (data collection → training → deployment)
Alongside this, I also have a solid foundation in:
✔️Machine Learning & Deep Learning
✔️Mathematics & Statistics (for model understanding and optimization)
✔️Python ecosystem (NumPy, Pandas, SciPy, etc.)
✔️API Development & Deployment(Docker, AWS, GCP)
I hold a Bachelor’s degree in Computer Engineering and am currently pursuing a Master’s in AI, which helps me stay aligned with the latest advancements in the field.
My approach is always to first understand the business problem and real-world constraints, and then design a solution that is accurate, scalable, and practical to use.
I care deeply about delivering solutions that actually work for clients not just in theory, but in real-world conditions.
Thanks & Regards,
Ruchir
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Delivery time starts when Ruchir receives requirements from you.
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Revisions may occur after the delivery date.
Scope binding of the project