Co-Sight is an open-source AI platform from ZTE AI Cloud designed for building, deploying, and managing real-time video analytics pipelines. It supports multi-model fusion, scalable deployment with Docker and Kubernetes, and low-latency inference across edge and cloud environments. With modular components for data ingestion, preprocessing, model training, and monitoring, Co-Sight accelerates development of smart city surveillance, traffic monitoring, and industrial inspection applications.
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Co-Sight
Co-Sight is an open-source AI platform from ZTE AI Cloud designed for building, deploying, and managing real-time video analytics pipelines. It supports multi-model fusion, scalable deployment with Docker and Kubernetes, and low-latency inference across edge and cloud environments. With modular components for data ingestion, preprocessing, model training, and monitoring, Co-Sight accelerates development of smart city surveillance, traffic monitoring, and industrial inspection applications.
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What is Co-Sight?
Co-Sight is an open-source AI framework that simplifies development and deployment of real-time video analytics solutions. It provides modules for video data ingestion, preprocessing, model training, and distributed inference on edge and cloud. With built-in support for object detection, classification, tracking, and pipeline orchestration, Co-Sight ensures low-latency processing and high throughput. Its modular design integrates with popular deep learning libraries and scales seamlessly using Kubernetes. Developers can define pipelines via YAML, deploy with Docker, and monitor performance through a web dashboard. Co-Sight empowers users to build advanced vision applications for smart city surveillance, intelligent transportation, and industrial quality inspection, reducing development time and operational complexity.
Who will use Co-Sight?
- AI developers
- System integrators
- DevOps engineers
- Enterprises in smart city and industrial IoT
- Data scientists interested in video analytics
How to use the Co-Sight?
- Step1: Clone the Co-Sight repository from GitHub
- Step2: Install dependencies via Docker and configure Docker Compose
- Step3: Define the video analytics pipeline YAML file
- Step4: Import or train AI models for detection, tracking, or classification
- Step5: Deploy pipelines on edge or cloud using provided Kubernetes manifests
- Step6: Monitor performance and logs through the built-in web dashboard
Platform
- Linux
Co-Sight's Core Features & Benefits
The Core Features
- Video data ingestion and preprocessing
- Pipeline orchestration via YAML definitions
- Multi-model fusion for detection, tracking, classification
- Distributed inference on edge and cloud
- Scalable deployment with Docker and Kubernetes
- Built-in monitoring dashboard
The Benefits
- Modular architecture accelerates development
- Low-latency, high-throughput video analytics
- Seamless integration with deep learning libraries
- Open-source and community-driven
- Reduces operational complexity
- Scales across edge and cloud environments
Co-Sight's Main Use Cases & Applications
- Smart city surveillance
- Traffic flow monitoring
- Industrial quality inspection
- Retail foot traffic analysis
FAQs of Co-Sight
Co-Sight Company Information
- ZTE Corporation
Co-Sight Reviews
5/5
Co-Sight's Main Competitors and alternatives?
NVIDIA DeepStream
Intel OpenVINO
AWS Panorama
Baidu EasyDL
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