Encasement-Based Point Tracking for Robust Object Motion
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Solution Overview
Problem
Existing object tracking methods rely on contoured edges, which may not effectively track the motion of objects or encasements over time, especially when both the object and camera are moving, as they do not directly follow the encasement or object's motion.
Innovation Solution
A system and method that designate an encasement in an image, identify significant and persistent points within it, track these points over time based on their characteristics, and determine the new position of the encasement by scaling and shifting transformations to maximize overlap between images, allowing for accurate determination of the encasement's location despite changes in camera position or object movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If contoured edges of the object are used for tracking, then the tracking method can follow the motion of the object, but the method fails to accurately track when both the object and camera are moving
Solution Approach 1:
The patent introduces an encasement as an intermediary construct that bounds the object of interest. Instead of tracking the object directly through its contoured edges, the system tracks the encasement's location and uses this information to determine object motion. This intermediary approach allows the system to separate object tracking from camera motion, resolving the contradiction between tracking accuracy and adaptability to camera motion.
Solution Approach 2:
The patent segments the tracking problem into distinct components: (1) identifying and bounding the object with an encasement, (2) tracking significant points within the encasement, and (3) determining encasement location from point positions. This segmentation allows each component to be optimized independently, improving overall tracking reliability while maintaining adaptability to camera motion.
2Reliability
If significant points are identified using local gradient magnitude thresholding, then the points can be reliably identified, but the computational complexity increases due to evaluating multiple directions
Solution Approach 1:
The patent applies partial action by evaluating local gradients in a limited set of directions (typically horizontal and vertical) rather than all possible directions. This partial evaluation provides sufficient information to identify significant points reliably while keeping computational complexity manageable. The approach uses exactly enough directional information needed for the task without excessive computation.
3Measurement precision
If scaling and shifting transformations are applied to maximize overlap between images, then the encasement location can be accurately determined, but the processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-identifying significant points within the encasement in the first image and predicting their positions in subsequent images based on encasement transformation. This preliminary setup allows the system to quickly compare expected point positions with actual detected points, reducing processing time while maintaining high measurement precision for encasement location determination.
Data Source
AI summary
Systems and methods for tracking points within an encasement are provided. According to an aspect of the invention, a processor designates an encasement at a first location within a first image acquired at a first time; identifies points to track within the encasement; determines characteristics of the points to track; tracks the points over time based on the characteristics; and determines a second location of the encasement within a second image acquired at a second time based on positions of the tracked points at the second time. Identifying the points to track may include identifying points within the encasement that are significant and persistent.