Target Object Detection Using Human Body Key Points and Item Position Changes
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Solution Overview
Problem
In smart shops, existing technologies fail to accurately determine which customer picks up which item from shelves without queuing, necessitating a method to identify item movement and associate it with the correct customer.
Innovation Solution
A method involving image frame sequence determination based on position change detection, human body key point detection, and target object identification using distance analysis between human body key points and items, with image acquisition devices and sensors to track item movement and customer interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to track customer-item interactions, then system simplicity is maintained, but accuracy in identifying which customer picks up which item deteriorates
Solution Approach 1:
The patent segments the customer-item interaction detection into multiple independent modules: image acquisition devices capture visual data, sensors detect item position changes, human body key point detection algorithms identify customer actions, and trajectory analysis modules track movement patterns. Each module handles a specific aspect of the detection task, improving overall accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces several intermediary elements to bridge the gap between simple monitoring and accurate identification: image frames serve as intermediaries between physical customer actions and digital records, key point detection results act as intermediaries between raw image data and customer identification, and trajectory points serve as intermediaries between continuous movement and discrete event detection. These intermediaries enable accurate tracking without requiring direct complex interaction between all system components.
2Measurement precision
If continuous image analysis is performed to accurately identify target objects, then identification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
Instead of continuous analysis, the system performs periodic image frame analysis triggered by sensor events. When a sensor detects an item position change, the system then analyzes the relevant image frames to identify the target customer. This event-driven periodic analysis maintains high identification accuracy while significantly reducing processing time compared to continuous analysis, as computation is only activated when necessary.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing image frames in sequences, and pre-identifying potential key points in images before actual customer-item interaction detection is needed. This allows the system to quickly retrieve and analyze only relevant pre-processed data when sensors trigger detection events, rather than processing raw images from scratch, thereby reducing real-time processing time while maintaining accuracy.
Data Source
AI summary
Embodiments of the present disclosure disclose a method and an apparatus for determining a target object and a human-computer interaction system. The method according to one embodiment of the present disclosure comprises: in response to detecting a position change of an item, determining a to-be-detected image frame sequence based on a detection moment when the position change is detected; performing a human body key point detection to a to-be-detected image frame in the to-be-detected image frame sequence; and determining a target object which performs a target operation action to the item based on a detection result of the human body key point detection. This embodiment improves accuracy of the determined target object.