Mixed-Filter Object Tracking for Rapid Deformation
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Solution Overview
Problem
Existing object tracking methods face challenges such as instability due to errors in selecting auxiliary frames in transductive learning and inability to handle sudden deformations in inductive learning, as well as issues like scale conversion, background interference, lighting changes, rotation, and deformation, which affect tracking accuracy.
Innovation Solution
A two-branch framework combining short-term filters predicted based on transductive learning and long-term filters optimized through inductive learning, with a mixed filter generated by fusing both to enhance stability and discrimination in object tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If transductive learning is used to predict filters based on auxiliary frames, then tracking speed is improved, but tracking stability deteriorates due to errors in selecting auxiliary frames
Solution Approach 1:
The filter prediction process is segmented into two distinct branches: a short-term branch using transductive learning for speed, and a long-term branch using inductive learning for stability. Each branch operates independently with its own filter prediction mechanism, allowing the system to leverage the speed advantage of transductive learning while mitigating its instability through the complementary long-term branch.
Solution Approach 2:
The short-term and long-term filter branches are merged through response map fusion. The system generates response maps from both branches and combines them using a fusion module that integrates the immediate responsiveness of the short-term branch with the structural stability of the long-term branch, achieving both speed and reliability.
2Adaptability or versatility
If inductive learning is used to optimize filters, then ability to handle sudden deformations is improved, but tracking speed deteriorates
Solution Approach 1:
The system segments the filter optimization task by applying inductive learning exclusively to the long-term branch, which handles sudden deformations and structural changes. The short-term branch continues to use transductive learning for rapid tracking of stable objects, thus achieving deformation handling capability without sacrificing overall tracking speed.
Solution Approach 2:
The system dynamically selects which branch to prioritize based on the tracking situation. When sudden deformations are detected, the long-term inductive learning branch becomes more influential in the fusion process, while during stable tracking periods, the short-term transductive learning branch dominates, optimizing speed. This dynamic adaptation allows the system to handle deformations effectively without continuous speed penalty.
3Device complexity
If a single filter is used for tracking, then device complexity is reduced, but tracking accuracy deteriorates under varying conditions such as scale conversion, background interference, lighting changes, rotation, and deformation
Solution Approach 1:
The tracking system is segmented into two specialized filter branches: a short-term filter for rapid response to current frame information and a long-term filter for capturing structural patterns and handling deformations. This segmentation allows each filter to be optimized for its specific function, improving overall tracking accuracy under varying conditions while maintaining manageable complexity through modular design.
Solution Approach 2:
The system creates a composite filtering mechanism by combining the short-term and long-term filters. Similar to composite materials that combine different materials to achieve superior properties, this composite filter structure combines the speed advantage of the short-term filter with the adaptability advantage of the long-term filter, achieving high tracking accuracy across diverse conditions while keeping individual filter components relatively simple.
Data Source
AI summary
A method and apparatus with object tracking is provided. The method includes generating a mixed filter by fusing a short-term filter with a long-term filter; and performing object tracking on a current frame image based on the mixed filter. The short-term filter is dependent on a prediction of the current frame image in a video sequence, and the long-term filter is a previously generated long-term filter or is generated by optimizing the previously generated long-term filter based on an object template feature pool.