Shopping Cart Pose Detection for Triggered Item Recognition
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Solution Overview
Problem
Automated checkout systems in shopping carts face challenges due to the computational intensity of item identification, which depletes constrained resources and battery power, necessitating a solution to conserve resources while ensuring efficient and precise item detection.
Innovation Solution
An automated checkout system uses a pose detection model to predict user actions, triggering item identification only when cart contents change, employing machine learning models to efficiently update the shopping list without excessive resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used for item identification in automated checkout systems, then item detection accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary pose detection on the user before triggering item identification. By detecting user pose in advance and using it to predict potential item changes, the system prepares the necessary context information beforehand, allowing item identification to be triggered only when actually needed rather than continuously.
Solution Approach 2:
User pose detection serves as an intermediary mechanism between continuous monitoring and item identification. The pose detection model acts as a filter that translates user behavior into actionable triggers, mediating between the need for accurate item detection and the constraint of limited computational resources.
2Reliability
If item identification is performed continuously to ensure accurate tracking, then item detection reliability is improved, but battery power depletes faster
Solution Approach 1:
Instead of continuous item identification, the system implements periodic action by triggering identification only when pose detection indicates a potential item change. This periodic triggering based on user behavior events maintains tracking reliability while significantly reducing battery power consumption compared to continuous operation.
3Productivity
If pose detection is used as a trigger mechanism, then computational efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the item tracking process into distinct stages: pose detection stage and item identification stage. This segmentation allows each component to operate independently with optimized resource allocation, improving overall computational efficiency while managing system complexity through modular architecture.
Data Source
AI summary
A system may access a first set of images captured by cameras coupled to a shopping cart, wherein each image depicts a user associated with the shopping cart. A system may apply a pose detection model to each of the images to predict a user's pose. A system may apply an action prediction model to the set of images and the predicted poses to predict whether the user performed an action to change the contents of a storage area of the shopping cart. A system may, responsive to predicting that the user performed a change action, apply an item identification model to a second set of images of a storage area of the shopping cart to identify an item associated with the change action. A system may update an item list of the user based on the change action and the identified item.