Multi-Sensor Object Position Integration for Occlusion Handling
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Solution Overview
Problem
Current techniques for real-time transmission of competition space information suffer from inaccurate object position information due to measurements from a single viewpoint, leading to poor control of video and audio material positioning and corrupted depth expressions, especially when occlusion occurs.
Innovation Solution
An information integration method that synchronously transmits media and sensor information across spaces, using multiple sensors to calculate smallest or largest rectangles surrounding objects based on position information from various viewpoints to compensate for missing data and reduce occlusion effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If position information is measured from only one viewpoint, then the measurement process is simple, but the accuracy of object position information deteriorates and occlusion cannot be properly handled
Solution Approach 1:
The patent transitions from single-viewpoint (2D) measurement to multi-viewpoint (3D) measurement by adding spatial dimensions. Multiple sensors are positioned at different locations around the competition space, capturing position information from multiple angles and depths, thereby resolving occlusion and improving measurement accuracy.
Solution Approach 2:
The measurement system is segmented into multiple independent sensors positioned at different viewpoints. Each sensor independently measures position information from its own perspective, and the results are integrated to form a comprehensive understanding of object positions, eliminating the limitations of single-viewpoint measurement.
2Reliability
If multiple sensors from multiple locations are used, then object position accuracy improves and occlusion is resolved, but the system complexity and data processing burden increase
Solution Approach 1:
Position information from multiple sensors is merged into a unified coordinate system. The patent integrates data from multiple viewpoints by transforming local coordinate measurements into a common reference frame, combining the reliable measurements from each sensor to produce accurate, comprehensive position information.
Solution Approach 2:
A coordinate transformation mechanism serves as an intermediary that converts position information from different sensor viewpoints into a unified reference frame. This mediator enables seamless integration of multi-sensor data while managing the complexity of coordinating multiple measurement systems.
3Measurement precision
If smallest rectangles are calculated for overlapping objects, then occlusion discrimination improves, but calculation complexity increases compared to single-object processing
Solution Approach 1:
The patent performs preliminary actions by pre-establishing the coordinate system framework and sensor positioning before actual measurement. This preparation enables efficient real-time processing during competitions, as the transformation matrices and reference frames are already established, allowing rapid calculation of smallest rectangles for overlapping objects.
Data Source
AI summary
An influence on object expression performed at a transmission destination is reduced. A position information reception unit 11 of an information integration device 1 receives, regarding objects which are measured by a plurality of sensors from a plurality of locations and overlap in any of the locations, position information for each location on areas of the objects. A position information integration unit 13 calculates smallest rectangles or largest rectangles surrounding the objects by using the position information for each location.