DeepCamera: Transforming Traditional Surveillance with Cutting-edge AI
DeepCamera is a revolutionary project designed to enhance traditional surveillance systems using the power of state-of-the-art AI technologies. It transforms conventional cameras and CCTV systems into intelligent devices capable of performing advanced tasks such as facial recognition, fall detection, and parking lot monitoring. The project is open-source and aims to make these AI-driven capabilities easily accessible and deployable on devices at the edge.
Key Features of DeepCamera
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AI Empowerment for Any Camera: DeepCamera brings facial and person recognition (RE-ID), parking lot management, and fall detection capabilities to existing camera systems. More features are continually being added.
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Machine Learning Pipeline for AI Camera/CCTV: The project uses advanced tools like Milvus, a vector database, for feature clustering and Labelstudio for labeling data. This simplifies the development of AI camera solutions.
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User-friendly Edge AI Environment: Utilizing Docker, DeepCamera offers a comprehensive development environment where AI frameworks and desktop applications are easy to deploy without the need for additional software installations.
Applications of DeepCamera
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Self-supervised Person Recognition for Intruder Detection: A key application is SharpAI yolov7_reid, which leverages AI tech to detect intruders using existing surveillance systems. It uses technologies like Yolov7 for detecting persons, FastReID for feature extraction, and Milvus for learning and identifying unknown individuals. It integrates with smart home systems via Home-Assistant.
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Facial Recognition for Intruder Detection: This feature can be deployed locally, ensuring all data is stored on-site, enhancing security and privacy.
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Enhanced CCTV Monitoring with Cloud Support: Users can start using DeepCamera by registering on the SharpAI website and accessing cloud features that support facial recognition and intrusion detection for free.
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Laptop Screen Monitor for Safety: Designed to monitor screen content for kids and teens, this application extracts screen image features, saving them locally for analysis. It supports labeling and model training for safety applications.
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Person Detector: Another application is detecting individuals using existing camera systems.
Supported Devices
DeepCamera supports a wide range of devices, including Nvidia Jetson (Nano, Xavier AGX), Raspberry Pi, various Intel and AMD platforms (MacOS, Windows, Ubuntu), and several types of cameras and CCTV systems like RTSP, Blink, and Google Nest.
Setup and Installation
Setting up DeepCamera is straightforward. By installing the sharpai-hub Python package and following a few command-line instructions, users can deploy various AI applications, including yolov7_reid for person recognition. Detailed installation guides and prerequisites, such as Docker and Python installations, are available to assist in the process.
Commercial and Community Support
For organizations seeking advanced implementations, DeepCamera offers commercial support including model customization, edge clustering, and additional features like voice application pipelines and behavior analysis models. The community also supports general queries and provides assistance for deploying RTSP camera feeds and more.
Join the Community
DeepCamera invites users to join their Slack channel for community and commercial support, fostering collaboration and assistance in implementing and optimizing AI camera applications.
Through DeepCamera, the promise of AI-driven surveillance becomes an accessible reality, ensuring security and efficiency in a wide range of environments.