Domain Adaptation for Drone Video Action Recognition
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Solution Overview
Problem
Human action recognition in drone videos is challenging due to the lack of annotations in target domain videos, making it difficult to adapt classifiers trained on third-person videos to drone videos, which often have limited and costly annotated data.
Innovation Solution
A computer-implemented method that learns a transformation to align the target drone domain with the source non-drone domain by adding a learnable transformation block to the video classification network, allowing the classifier to better adapt to drone videos and reduce the need for extensive annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a classifier trained on third-person videos is directly applied to drone videos, then the deployment time and cost are reduced, but the recognition performance deteriorates due to domain differences and lack of annotations
Solution Approach 1:
The system performs preliminary domain adaptation by learning a transformation that maps drone video features to third-person video feature space before classification. This preliminary transformation aligns the target domain (drone videos) with the source domain (third-person videos), enabling the pre-trained classifier to work effectively on drone videos without requiring retraining or extensive annotations.
Solution Approach 2:
The invention introduces an intermediate transformation layer that acts as a mediator between the drone video domain and the third-person video domain. This transformation block learns to bridge the domain gap by mapping features from the target domain to the source domain, allowing the classifier to operate in the familiar source domain space while processing target domain data.
2Reliability
If extensive annotations are collected for drone videos to improve recognition performance, then the reliability improves, but the cost and time required for data preparation increase significantly
Solution Approach 1:
The system creates a virtual copy of the source domain feature distribution by learning a transformation that maps target domain features to source domain features. Instead of collecting and annotating extensive drone video data, the method copies the statistical properties and feature distributions of the well-annotated third-person video domain and applies them to the drone video domain through the learned transformation.
Solution Approach 2:
The invention changes the parameter space by learning a transformation that operates on feature representations rather than raw data. This transformation modifies the feature parameters to align with the source domain, enabling effective classification without changing the underlying data or requiring extensive annotations in the target domain.
3Adaptability or versatility
If domain adaptation transformation is learned and applied, then the adaptability to drone videos improves, but the device complexity increases due to additional transformation blocks
Solution Approach 1:
The system addresses domain adaptation by operating in the feature dimension rather than modifying the video data dimension. The transformation block learns to map features from the target domain to the source domain in the feature space, adding adaptability without requiring complex architectural changes to the overall network structure.
Solution Approach 2:
The learned transformation block serves multiple functions: it acts as a domain adapter, a feature aligner, and a domain invariant feature extractor. This single transformation component handles multiple aspects of domain adaptation, reducing the need for separate modules for each function and thereby limiting the increase in overall system complexity.
Data Source
AI summary
A method is provided for drone-video-based action recognition. The method learns a transformation for each of target video clips taken from a set of target videos, responsive to original features extracted from the target video clips. The transformation corrects differences between a target drone domain corresponding to the target video clips and a source non-drone domain corresponding to source video clips taken from a set of source videos. The method adapts the target to the source domain by applying the transformation to the original features to obtain transformed features for the target video clips. The method converts the original and transformed features of same ones of the target video clips into a single classification feature for each of the target videos. The method classifies a human action in a new target video relative to the set of source videos using the single classification feature for each of the target videos.