Adaptive Video Object Detection via Self-Training Fusion
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Solution Overview
Problem
Existing object detection systems for video sequences struggle to adapt to changing environments without relying on human-labeled data, as online learning requires continuous human input, which is labor-intensive and often impractical.
Innovation Solution
A system that uses a classification-based learning approach, processing video sequences to generate detections, fuse them using methods like priority or mean fusion, and update parameters to adapt to changing conditions, allowing for automatic labeling and reducing dependency on human annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human-labeled data is used for online training, then the system can adapt to changing environments, but the process becomes labor-intensive and physically impossible in real-time
Solution Approach 1:
The system performs self-training by using its own detection outputs as training data. The detection module generates candidate object locations, which are then used to automatically update the classifier parameters without requiring external human annotation, enabling the system to adapt autonomously to changing environments
Solution Approach 2:
The patent introduces an intermediary training process that bridges detection and adaptation. Instead of direct human labeling, the system uses detection results as intermediate training signals, allowing automatic parameter updates while maintaining adaptability to environmental changes
2Measurement precision
If human annotations are used for training, then classification accuracy can be improved, but the system becomes dependent on continuous human input
Solution Approach 1:
The classifier module automatically updates its own parameters using detection results as training data. This self-service mechanism maintains high classification accuracy while completely eliminating dependency on continuous human input, as the system retrains itself using its own operational data
Solution Approach 2:
The system implements a feedback loop where detection results are fed back into the training process. The detection module's outputs become the training data for updating classifier parameters, creating a closed-loop system that maintains accuracy without external human intervention
3Measurement precision
If multiple detection methods are fused, then detection accuracy improves, but the system complexity increases
Solution Approach 1:
The patent combines multiple detection methods (color-based, texture-based, motion-based detectors) into a unified detection framework. By merging these different detection approaches, the system achieves improved detection accuracy while managing complexity through integrated processing
Data Source
AI summary
Described is a system for object detection using classification-based learning. A fusion method is selected, then a video sequence is processed to generate detections for each frame, wherein a detection is a representation of an object candidate. The detections are fused to generate a set of fused detections for each frame. The classification module generates a classification score labeling each fused detection based on a predetermined classification threshold. Otherwise, a token indicating that the classification module has abstained from generating a classification score is generated. The scoring module produces a confidence score for each fused detection based on a set of learned parameters from the learning module and the set of fused detections. The set of fused detections are filtered by the accept-reject module based on one of the classification score or the confidence score. Finally, a set of final detections representing an object is output.