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A web app front end built in fewer than four months that serves as an efficient annotation tool facilitating training ML models to monitor customers’ movements in a multi-camera store setting, and can run on browsers such as Google Chrome, Mozilla Firefox, and Safari.
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A back-end solution created using Node.js that fetches data from the client’s server
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A video and text file storage system relying on MongoDB
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A 3D reconstruction and skeleton visualization capability for action recognition
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A keypoint creation/edit feature that is vital to delivering error-free pose estimation, tracking and action recognition
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A multi-view function providing better occlusion handling and thereby boosting the ML model’s ability to correctly track customers’ movement
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A simple yet functional UI built with ReactJS that speeds up annotation and improves its efficiency
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A rigorous UI/UX testing process resulting in a few enhancements that improved the app’s performance
