Ai Door Control
2 days ago
**Job Requirements**PROJECT OVERVIEW**
AI-based door control system uses computer vision and deep learning for boardings & exits, by processing real-time video feeds and to detect people & objects in doorways. It replaces multiple traditional sensors with a single AI-powered solution.
The project includes deep learning models for people & object recognition, movement analysis, and real-time processing and decision making and logging. The system processes video data on the edge, needs models optimized for embedded deployment while maintaining detection accuracy and speed. The system must operate reliably across varying lighting conditions and handle complex scenarios such as multiple passengers, carried objects, and potential obstructions while maintaining strict safety standards.
**TASK DESCRIPTIONS**
Task 1: Data Analysis and Annotation
1.1 Data Categorization
1.2 Data Annotation
Task 2: Model Evaluation and Performance Analysis
2.1 Comprehensive Testing
- Generate detailed daily performance reports including:
2.2 Issue Analysis
- Create detailed documentation of all detection failures and performance issues, including:
- Analyze and document factors affecting model performance, creating a comprehensive issue categorization system
- Develop detailed recommendations for model improvements based on observed failure patterns
Task 3: Model Enhancement and Optimization
3.1 Data Preparation
3.2 Model Development
Task 4: Extended Vision Capabilities
**Work Experience**Required Skillset
- Strong expertise in computer vision and deep learning, specifically with PyTorch / TensorFlow and object detection models
- Experience in model optimization, testing, and deployment on edge devices
- Proficiency in MLOps tools including AWS Sage Maker, DVC, and version control systems
Input
- Existing labeled dataset from current deployments
- New Video data from exiting installations
- Current model architecture and weights
- System performance requirements and specifications
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