Object Detection via Movement Vector Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Natural user input devices, such as 3D cameras and eye tracking systems, suffer from imprecision in object selection due to sensor limitations and pointing inaccuracies, leading to prolonged adjustment processes.
Innovation Solution
A method that uses data points from natural user input devices to determine movement vectors, predict target areas, and assign interaction probabilities to objects, allowing for accurate selection without exact pointing precision, utilizing vector analysis and visual feedback to enhance selection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural user input devices are used for object selection, then user interaction becomes more intuitive and accessible, but measurement precision and selection accuracy deteriorate due to sensor limitations and pointing inaccuracies
Solution Approach 1:
The system performs preliminary actions by tracking user movement in advance and predicting the target object before the user completes the pointing action. The movement tracking module continuously monitors user input device position, and the target prediction module uses this historical movement data to anticipate which object the user intends to select, resolving the contradiction by providing accurate selection before precise pointing is achieved.
Solution Approach 2:
The system implements feedback by providing visual indicators that show the predicted target object to the user during the selection process. This feedback loop allows the user to see what object the system predicts they want to select, enabling correction or confirmation of the prediction, thereby maintaining high selection accuracy despite imprecise pointing input.
2Measurement precision
If visual feedback is provided to improve selection accuracy, then measurement precision increases, but loss of time increases due to prolonged adjustment processes
Solution Approach 1:
The system performs preliminary target prediction based on tracked movement patterns before the user completes their pointing action. By analyzing the trajectory and velocity of the user input device, the system anticipates the target object in advance, providing visual feedback that confirms or corrects the prediction. This eliminates the need for prolonged adjustment processes while maintaining high selection accuracy.
3Ease of operation
If the size of objects and spacing are increased to compensate for imprecision, then ease of operation improves, but area of stationary object increases which may not be desirable for all interfaces
Solution Approach 1:
The system replaces the mechanical approach of increasing object sizes with an intelligent prediction system. Instead of enlarging objects to make them easier to select, the system uses algorithms that track user movement patterns and predict the intended target. This substitution maintains the original object sizes and layout while achieving ease of operation through predictive accuracy.
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
A method for detecting objects (28) a person has interest in interacting with comprises: receiving data points (38) of a natural user input device (26) adapted for tracking a movement of the person; determining at least one vector (40) from the data points (38) by grouping data points (38) into groups (42), wherein the data points (38) of a group (42) indicate a user movement in one direction; determining a map (44) of interaction objects (28), such that data points (38) are mapable to interaction objects (28); predicting a target area (48) from the at least one vector (40) by mapping the at least one determined vector (40) to the map (44) of interaction objects (28); and predicting at least one interaction object (28') at the target area (48) as interaction object of interest.