Research on Person Recognition Model of Domain Adaptive Learning
- Resource Type
- Conference
- Authors
- Jiao, Ming-hai; Duan, Wei-ming; Wang, Jue; Luo, Ben-dong; Zhang, Chi; Sun, Xiang-yu
- Source
- 2020 Chinese Control And Decision Conference (CCDC) Chinese Control And Decision Conference (CCDC), 2020. :385-390 Aug, 2020
- Subject
- General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Adaptation models
Training
Data models
Object detection
Feature extraction
Detectors
Adaptive systems
Person recognition
Deep self training
Domain adaptive learning
Loss function
- Language
- ISSN
- 1948-9447
The person recognition problem in different domain is a hard challenge on lower accuracy of object detection with massive image data, that is the gap between person target and background is small and hard to distinguish different posture of person target. The novel domain adaptive person detection model based on transfer data training is proposed for detecting data on new target domain. And domain adaptive loss function is designed to control the difference between the two models with self training, so as to avoid catastrophic forgetting in the source domain. And then the experiment results show that the improved deep self training domain adaptive learning model and loss function algorithm are effective and efficient for person recognition problem.