Adaptive Boosted Classifier for Object Tracking Scene Changes
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Solution Overview
Problem
Conventional object tracking methods struggle to adapt to scene changes over time, particularly in dynamic environments like outdoor scenes where lighting and background conditions vary, leading to inefficiencies in updating classifiers and potential loss of useful weak classifiers.
Innovation Solution
The method involves adaptively boosting classifiers by updating weak classifiers over time using new samples while retaining old samples that are still representative, allowing for real-time adaptation to scene changes without discarding entire classifiers, thus maintaining effective tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional classifier based methods update strong classifiers by adding and deleting weak classifiers over time, then the system can adapt to scene changes, but useful weak classifiers may be deleted prematurely and computational complexity increases
Solution Approach 1:
The patent applies the discarding and recovering principle by retaining deleted weak classifiers in a repository and recovering them when they become useful again. When scene conditions return to previous states, previously deleted weak classifiers are reused instead of being permanently discarded, thus maintaining reliability while adapting to scene changes.
Solution Approach 2:
The system changes the parameter of weak classifier lifecycle from permanent deletion to temporary retention with recovery capability. By modifying the deletion parameter to include a recovery mechanism, the system achieves better adaptability without losing useful classifiers, resolving the contradiction between adapting to scene changes and retaining useful classifiers.
2Measurement precision
If conventional methods delete weak classifiers with error greater than threshold, then classification accuracy is maintained, but the possibility of using effective classifiers later is eliminated
Solution Approach 1:
The patent applies beforehand cushioning by creating a safety buffer through the repository that stores weak classifiers before they are permanently discarded. When a weak classifier exceeds the error threshold, it is cushioned in the repository rather than immediately deleted, preserving it for future use when conditions may make it effective again, thus maintaining both accuracy and adaptability.
Solution Approach 2:
The system implements discarding and recovering by temporarily discarding weak classifiers that exceed error thresholds into a repository rather than permanent deletion. These classifiers can be recovered and reused when scene conditions change, balancing the need for classification accuracy with the need to maintain adaptable classifiers for future scenarios.
3Adaptability or versatility
If Grabner et al. update classifier parameters using Kalman filtering, then adaptation to scene changes is achieved, but the updating mechanism becomes complicated and slow for higher dimensional classifiers
Solution Approach 1:
The patent extracts the core adaptation function from complex Kalman filtering and implements it through a simpler repository-based mechanism. Instead of using sophisticated parameter updating with Kalman filtering, the system extracts and stores weak classifiers in a repository, achieving adaptation through selective retrieval and reuse, thus reducing complexity while maintaining adaptability.
Solution Approach 2:
The system uses copying by creating copies of weak classifiers and storing them in the repository rather than continuously updating their parameters. This copying approach avoids the computational complexity of parameter updating mechanisms like Kalman filtering, while still enabling adaptation through selective use of copied classifiers in different scene conditions.
4Measurement precision
If AdaBoost trains weak classifiers on increasingly difficult training data, then a strong classifier is produced that is better than weak classifiers alone, but the system struggles to adapt to scene changes over time
Solution Approach 1:
The patent applies dynamics by making the classifier system dynamic through the repository mechanism. Instead of a static strong classifier trained once on difficult data, the system dynamically adapts by retrieving and using appropriate weak classifiers from the repository based on current scene conditions, maintaining both high performance and adaptability to changes over time.
Data Source
AI summary
A method adapts a boosted classifier to new samples. A boosted classifier is trained using initial samples. The boosted classifier is a combination of weak classifiers. Each weak classifier of the boosted classifier is updated adaptively by adding contributions of new samples and deleting contributions old samples.