Hybrid Object Trajectory Tracking Across Occlusion Intervals
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Solution Overview
Problem
Existing methods for tracking object trajectories, such as GPS and image-based location determination, suffer from inaccuracies and limitations, particularly in situations like occlusion, leading to unreliable and imprecise tracking results.
Innovation Solution
A hybrid method combining image-based and sensor-based location information, where confidence intervals are determined using image data, and non-confidence intervals are handled with sensor data, with error correction and matching algorithms to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image-based location determination is used, then visual tracking capability is improved, but accuracy deteriorates in occlusion situations
Solution Approach 1:
The patent combines image-based location determination with sensor-based location determination (GPS, inertial sensors) into a hybrid tracking system. The server integrates both types of data to determine object trajectories, leveraging the visual capability of image-based methods and the reliability of sensor-based methods during occlusions, thereby resolving the contradiction between tracking reliability and location accuracy.
2Duration of action of moving object
If sensor-based location determination is used, then tracking continuity is improved, but accuracy deteriorates
Solution Approach 1:
The patent applies different quality standards to different portions of the tracking data. Image-based location information is used when confidence intervals indicate high reliability (improving accuracy), while sensor-based information is used during occlusion periods (maintaining continuity). This local differentiation resolves the contradiction between tracking continuity and location accuracy.
3Measurement precision
If hybrid method is used, then location accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces a server as an intermediary that automatically integrates image-based and sensor-based location information. The server performs confidence interval determination, data fusion, and trajectory calculation, managing the complexity of the hybrid system centrally rather than requiring complex coordination between multiple distributed components, thus resolving the contradiction between location accuracy and system complexity.
Data Source
AI summary
There is provided a method for tracking a trajectory of an object during a target time interval, based on image based locational information and sensor based locational information. The method includes determining a confidence interval of the image based locational information, the confidence interval being at least a partial time interval in the target time interval, tracking the trajectory of the object during the confidence interval, based on the image based locational information, and tracking the trajectory of the object during a non-confidence interval which is an interval other than the confidence interval in the target time interval, based on the sensor based locational information.