Object Permanence Tracking for Occluded Environment Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Simulated environments in applications like virtual reality and robotics face challenges in maintaining accurate representations of objects over time, particularly when objects become occluded, leading to suboptimal performance and updates in environment models.
Innovation Solution
A method involving image sensors and processors that capture and analyze image data to determine object presence and occlusion, maintaining or removing object representations in the environment model based on whether the object is visible or occluded by other objects, ensuring the model remains accurate and robust.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the environment model is continuously updated with new image data, then the model accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by capturing image data at multiple timestamps and identifying object positions before the update decision is made. This allows the system to have object position information ready when evaluating whether to update the environment model, reducing the computational burden during the critical update decision phase.
Solution Approach 2:
Instead of continuously updating all objects in the environment model with every new image data, the system selectively updates only those objects that meet specific criteria (newly detected objects or objects with significant position changes). This partial action approach maintains model accuracy while significantly reducing unnecessary computational complexity.
2Reliability
If the system uses multiple image timestamps to detect object permanence, then the reliability of object detection is improved, but the time required for processing increases
Solution Approach 1:
The system performs preliminary actions by capturing and storing image data at multiple timestamps along with object position information before the object permanence determination is needed. This pre-processing allows the system to quickly reference already-analyzed data when making update decisions, reducing the time required at the critical decision point while maintaining high detection reliability.
Solution Approach 2:
The system segments the image processing task by analyzing different timestamps independently and storing the results in the environment model. By dividing the temporal data into discrete, pre-analyzed segments with identified object positions, the system avoids the need for complex real-time analysis of entire video sequences, thereby reducing processing time while maintaining reliable object detection.
Data Source
AI summary
Systems, methods, and computer program products for managing simulated environments are described. A simulated environment is accessed which represents a physical environment, and representations of objects are included, maintained, or removed in the simulated environment based on whether objects are represented in image data of the physical environment, and based on whether objects are occluded by other objects in the image data of the physical environment. Future occlusion can also be predicted based on motion paths of objects.