Online Training Object Tracking via Global Pattern Learning
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Solution Overview
Problem
Current object pose estimation methods for tracking objects in videos are not 100% accurate due to issues like occlusion and fast movement, leading to errors in target identification and tracking.
Innovation Solution
An online training model is used to learn global patterns of targets, incorporating a classifier for ID classification, and generates training data accumulated over time, employing both motion and appearance factors for accurate tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current object pose estimation methods are used for tracking objects in videos, then tracking can be performed, but accuracy is not 100% due to occlusion and fast movement causing errors in target identification
Solution Approach 1:
The system performs preliminary actions by accumulating training data over time and training the neural network model in advance with diverse samples including occluded and fast-moving targets. This preliminary training enables the model to maintain high accuracy and reliability during actual tracking operations despite challenging conditions like occlusion and fast movement.
Solution Approach 2:
The system implements feedback mechanisms by continuously accumulating training data from tracking results and using this data to retrain and improve the neural network model. The model learns from past tracking errors and successes, adjusting its parameters to reduce future errors in target identification and tracking accuracy.
2Measurement precision
If online training model with classifier is used to learn global patterns and generate accumulated training data, then accuracy and efficiency of object tracking is enhanced, but device complexity increases
Solution Approach 1:
The system employs dynamic characteristics by implementing an online training model that continuously adapts and updates the neural network parameters based on accumulated training data. The model dynamically adjusts its classification capabilities and global pattern recognition through incremental learning, improving tracking accuracy while managing complexity through adaptive rather than static processing.
Solution Approach 2:
The system utilizes parameter changes by modifying the neural network model parameters through online training with accumulated data. The classifier parameters and network weights are continuously adjusted based on new training samples, enabling the system to improve accuracy by learning from diverse patterns while managing complexity through parameter optimization rather than structural expansion.
Data Source
AI summary
A method and system for tracking an object in an input video using online training includes a step for training a classifier model by using global pattern matching, and a step for classifying and tracking each target through online training including the classifier model.