Pose-Aware Feature Learning for Video Object Recognition
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Solution Overview
Problem
Conventional feature learning methods for object recognition, such as clothing recognition, require costly manual annotation and suffer from the overfitting problem due to insufficient training samples, especially for deep learning models, and are not efficient in handling pose variations and light changes.
Innovation Solution
A pose-aware feature learning system that includes an object tracker, pose estimator, image pair generator, and neural network trainer, which automatically generates labeled image pairs from video frames to train a neural network without manual annotation, enabling the system to predict whether image pairs depict the same or different objects and pose differences, thus addressing overfitting and improving robustness to pose and light variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used for training deep learning models, then training data quality is improved, but cost and time consumption increase significantly
Solution Approach 1:
The system uses automated pose estimation and image pair generation to create training data without manual annotation. The pose estimator automatically detects and tracks poses across video frames, and the image pair generator automatically creates labeled image pairs, allowing the system to serve itself rather than requiring human annotators.
Solution Approach 2:
The patent replaces the mechanical process of manual annotation with automated computational processes. Instead of humans manually labeling data, the system uses pose estimation algorithms and image processing to automatically generate labeled training data, substituting mechanical human labor with automated mechanical systems.
2Quantity of substance
If insufficient training samples are used, then data collection cost is reduced, but model accuracy deteriorates due to overfitting
Solution Approach 1:
The system performs preliminary actions by automatically generating a large quantity of training samples from video data before model training. The image pair generator creates numerous labeled image pairs from video frames, ensuring sufficient training data is prepared in advance to prevent overfitting while maintaining high model accuracy.
3Device complexity
If conventional feature learning methods are used, then implementation simplicity is maintained, but robustness to pose variations and light changes deteriorates
Solution Approach 1:
The system changes the parameters of feature extraction by incorporating pose information and temporal context. Instead of using static image features, the system uses pose-aware features that include spatial relationships and temporal dynamics, enabling robust recognition across pose variations and lighting changes while maintaining reasonable implementation complexity.
Data Source
AI summary
A pose-aware feature learning system includes an object tracker which tracks an object on a subject in a plurality of video frames, a pose estimator which estimates a pose of the subject in a track of the plurality of video frames, an image pair generator which extracts a plurality of image pairs from the track of the plurality of video frames, and labels the plurality of image pairs with the estimated pose and as depicting the same or different object, and a neural network trainer which trains a neural network based on the labeled plurality of image pairs, to predict whether an image pair depicts the same or different object and a pose difference for the image pair.