Vision-Based Object Detector Domain Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vision-based object detection systems face challenges when transitioning from a source domain to a target domain with different conditions or camera positions, requiring re-training or domain adaptation to maintain accuracy, which is inefficient without sufficient labeled samples.
Innovation Solution
A method for unsupervised domain adaptation that involves acquiring videos from both source and target domains, performing change detection to collect unlabeled sample images, determining domain adaptation parameters based on domain shift properties, and adapting the vision-based object detector using these parameters to function effectively in the target domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the classifier is trained for detection at a particular site with specific conditions and camera position, then detection accuracy is improved, but the system cannot be applied to different sites or conditions without re-training
Solution Approach 1:
The patent transforms the classifier by learning a transformation matrix that maps features from the source domain to the target domain. This parameter transformation allows the classifier trained on source domain data to adapt to target domain conditions without re-training, resolving the contradiction between maintaining detection accuracy and achieving domain adaptability
Solution Approach 2:
The patent introduces an intermediate transformation step between the source domain classifier and target domain application. This intermediary transformation matrix serves as a bridge that adapts the classifier's feature space to match the target domain, enabling accurate detection across different sites and conditions without direct re-training
2Measurement precision
If re-training is performed to maintain accurate performance when domain changes, then detection accuracy is maintained, but time and resources are required to collect and label samples
Solution Approach 1:
The patent performs preliminary domain adaptation by learning the transformation matrix between source and target domains before actual target domain detection is needed. This preliminary transformation setup avoids the need for time-consuming re-training when deploying to new domains, as the adaptation is prepared in advance using unlabeled target domain samples
3Loss of time
If domain adaptation is used to reduce re-training effort, then re-training time is reduced, but detection accuracy may deteriorate without sufficient labeled samples
Solution Approach 1:
The patent implements self-service domain adaptation where the system automatically learns the domain transformation matrix using only unlabeled samples from the target domain. This self-supervised approach eliminates the need for manual labeling while maintaining detection accuracy, as the transformation is learned directly from the target domain data distribution
Data Source
AI summary
A method for detecting an object includes acquiring a first video of a source domain where a vision-based object detector is trained. Change detection is performed on the first video to collect one or more first localized, unlabeled sample images from the source domain. A second video is acquired of a target domain where the vision-based object detector is used. Change detection is performed on the second video to collect one or more second localized, unlabeled sample images from the target domain. A domain adaptation parameter is determined based on, one or more domain shift properties between the one or more first localized, unlabeled sample images from the source domain and the one or more second localized, unlabeled sample images from the target domain. The vision-based object detector is adapted to be used for object detection in the target domain based on the domain adaptation parameter.