Job details
About Signify Through bold discovery and cutting-edge innovation, we lead an industry that is vital for the future of our planet: lighting. Through our leadership in connected lighting and the Internet of Things, we're breaking new ground in data analytics, AI, and smart solutions for homes, offices, cities, and beyond. At Signify, you can shape tomorrow by building on our incredible 125+ year legacy while working toward even bolder sustainability goals. Our culture of continuous learning, creativity, and commitment to diversity and inclusion empowers you to grow your skills and career. Join us, and together, we’ll transform our industry, making a lasting difference for brighter lives and a better world. You light the way. More About The Role
- Collect and annotate datasets of people walking in and out of doors/entrances.
- Install a people count sensor and occupancy sensor above a door for data collection.
- Log occupancy data and videotape door activity 24/7 for reference.
- Manually annotate sensor data based on video footage whenever the occupancy sensor is triggered.
- Train a Recurrent Neural Network (RNN) with the annotated dataset.
- Test the RNN on a separate, unseen dataset to evaluate performance.
- Conduct real-time inference testing for the trained model.
- If successful, implement the RNN inference in VBI.
- Collaborate with machine learning engineers and other team members to refine the process and improve accuracy.
- Background in machine learning.
- Ideal for students seeking a graduation assignment or looking to specialize in AI Technology Architecture.
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Hiring company
Signify
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