Object Tracking Verification via Feature Point Reselection
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Solution Overview
Problem
Conventional object tracking techniques in digital video workflows are inefficient and prone to errors due to manual selection and deviation of feature points, especially with complex objects, leading to inaccurate tracking and significant user and computational resource consumption.
Innovation Solution
An object tracking system that automatically selects and verifies feature points using machine learning, reselecting them when deviation occurs to maintain accurate tracking without user intervention, employing feature point verification and reselection modules to ensure the object mask remains adhered to the object across frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual feature point selection is used, then tracking can be initiated, but substantial user effort and time are required and results are prone to error
Solution Approach 1:
The system automatically selects feature points using machine learning algorithms, eliminating the need for manual user selection. The algorithm independently identifies and tracks feature points across video frames, making the system self-sufficient and removing human intervention from the feature point selection process.
Solution Approach 2:
The patent replaces manual mechanical selection (user clicking/placing points) with an automated computational system based on machine learning. The machine learning model processes video data to automatically identify feature points, substituting human manual operations with an intelligent automated system.
2Productivity
If conventional tracking algorithms are used, then object tracking is performed, but feature points deviate from the object during movement causing inaccurate tracking
Solution Approach 1:
The system continuously verifies whether tracked feature points remain associated with the target object by analyzing spatial relationships and object boundaries in each frame. When deviation is detected, the system provides feedback by reselecting feature points to maintain accurate tracking, creating a closed-loop control system that corrects errors in real-time.
Solution Approach 2:
The patent implements dynamic feature point selection where the system adapts to changing conditions during video playback. Instead of using static pre-selected points, the system dynamically reselects feature points based on current frame analysis, allowing it to respond to rapid object movement, camera changes, and varying scene conditions.
3Extent of automation
If manual feature point selection is used, then initial tracking setup is possible, but significant user interaction is required which is inefficient
Solution Approach 1:
The system performs complete automatic feature point selection and tracking verification without requiring user interaction. The machine learning model independently handles the entire tracking process from initial feature point selection through continuous verification and reselection, making the system fully automated and eliminating user time investment.
4Reliability
If conventional tracking techniques are used, then basic object tracking is achieved, but computational resources are consumed without efficient verification mechanisms
Solution Approach 1:
The system performs selective verification of feature point tracking by focusing computational resources on verifying whether feature points remain associated with the target object, rather than processing all video data equally. This partial action approach concentrates computational effort where it is most needed for maintaining tracking accuracy.
Data Source
AI summary
Object tracking verification techniques are described as implemented by a computing device. In one example, feature points are selected on and along a boundary of an object to be tracked, e.g., in an initial frame of a digital video, which are referred to as “feature points.” Tracking of the feature points is verified by the computing device between frames. If the feature points have been found to deviate from the object, the feature points are reselected. To verify the feature points, a number of tracked features points in a subsequent frame is compared to a number of feature points used to initiate tracking with respect to a threshold. Based on this comparison, if a number of feature points is “lost” in the subsequent frame that is greater than the threshold, the feature points are reselected for tracking the object in subsequent frames of the video.