Video Object Tracking via Shape Propagation and Adjustment
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Solution Overview
Problem
Current methods for obtaining accurate ground-truth data for video analytics require manual annotation and re-identification of objects frame-by-frame, which is time-consuming and prone to errors.
Innovation Solution
A system that displays previous frames with indicated shapes of identified objects, allowing users to adjust and associate these shapes with objects in subsequent frames, reducing the need for manual re-identification and minimizing labeling errors by providing thumbnail images for re-association.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation and re-identification of objects frame-by-frame is performed, then accurate ground-truth data can be obtained, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary object identification and shape drawing in the first frame, then carries these shapes forward to subsequent frames. This preliminary action eliminates the need to re-identify objects from scratch in each frame, significantly reducing annotation time while maintaining accuracy through user verification and adjustment capabilities.
Solution Approach 2:
The system copies the shapes and object associations from the first frame to subsequent frames. By replicating the initial annotation work across multiple frames and allowing users to make necessary adjustments, the system preserves accuracy while dramatically reducing the repetitive manual effort required compared to frame-by-frame annotation.
2Measurement precision
If manual annotation is performed frame-by-frame, then complete object coverage can be achieved, but labeling errors increase due to fatigue and repetition
Solution Approach 1:
The system performs preliminary object identification in the first frame with full user attention and accuracy. By establishing correct object associations early and carrying them forward, the system avoids the fatigue-induced errors that occur during prolonged frame-by-frame annotation, while still achieving complete coverage through systematic frame processing.
Solution Approach 2:
The system provides visual feedback by displaying carried-forward shapes and allowing users to verify and adjust object associations in each frame. This feedback mechanism enables users to catch and correct potential labeling errors while maintaining consistent object tracking across frames, improving overall reliability without sacrificing completeness.
3Productivity
If automatic object tracking is implemented, then processing speed increases, but accuracy decreases due to occlusion and object re-identification challenges
Solution Approach 1:
The system enables self-service object tracking by automatically carrying forward shapes and associations from frame to frame without requiring continuous manual re-identification. This automated approach maintains high processing speed while preserving accuracy through user verification capabilities when objects become occluded or re-appear.
Solution Approach 2:
The system uses carried-forward shapes as intermediaries between automatic tracking and manual verification. These shapes serve as a bridge that maintains object associations across frames automatically while allowing user intervention when accuracy might be compromised, combining the speed of automation with the reliability of human judgment.
4Measurement precision
If re-identification is required in each frame, then object accuracy can be maintained, but the complexity of the annotation process increases
Solution Approach 1:
The system extracts the object identification task from each individual frame and consolidates it into a single first-frame annotation. By removing the repetitive re-identification requirement from subsequent frames and replacing it with automatic shape carrying forward, the system maintains object association accuracy while significantly simplifying the overall annotation process.
Solution Approach 2:
The system performs the complex object identification and association task preliminarily in the first frame, then simplifies subsequent frame processing to merely verifying and adjusting carried-forward shapes. This preliminary action approach maintains accuracy through thorough initial identification while reducing the complexity of ongoing annotation work.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for finding lost objects. In some implementations, a video frame is displayed. An input drawing a shape around an area of the video frame is received. A second video frame is displayed. An indication of the shape in the second video frame is displayed. An input to adjust the shape such that the shape is drawn around a second area is received.