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Two-Stage Pedestrian Detection Model Using a New Classification Head for Domain Generalization
Daniel Schulz1,2, Claudio A Perez1,2
1Department of Electrical Engineering, and Advanced Mining Technology Center, Universidad de Chile, Santiago 8370451, Chile.
Sensors (Basel, Switzerland)
|December 9, 2023
Summary
This study introduces a novel deep learning pedestrian detector that enhances domain generalization by using triplet loss to cluster pedestrian features. The new method achieves state-of-the-art results on the challenging CityPersons benchmark, particularly for heavy pedestrian scenarios.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Deep learning methods have significantly advanced pedestrian detection for applications like autonomous driving and surveillance.
- Existing detectors face challenges in domain generalization, limiting their performance across different environments.
Purpose of the Study:
- To develop a new two-stage pedestrian detector with improved domain generalization capabilities.
- To enhance feature representation by minimizing intra-class and maximizing inter-class distances using triplet loss.
Main Methods:
- Implemented a novel custom classification head with triplet loss integrated into Faster R-CNN and Cascade R-CNN architectures.
- Utilized the HRNet backbone pre-trained on ImageNet for feature extraction.
- Employed a progressive training pipeline, fine-tuning on progressively closer datasets to the target domain.
Main Results:
- Achieved state-of-the-art performance on the CityPersons benchmark.
- Obtained MR-2 scores of 9.9 (reasonable), 11.0 (small), and 36.2 (heavy).
- Demonstrated outstanding performance on the heavy subset, indicating robustness in challenging conditions.
Conclusions:
- The proposed triplet loss integration effectively improves domain generalization in pedestrian detection.
- The novel detector architecture and progressive training strategy yield superior results, especially in difficult scenarios.
