Datasets
Standard Dataset
RGBT234
- Citation Author(s):
- Submitted by:
- Weidai Xia
- Last updated:
- Tue, 02/25/2025 - 00:44
- DOI:
- 10.21227/hqj7-rd94
- License:
30 Views- Categories:
- Keywords:
Abstract
This dataset is a large-scale video benchmark constructed for RGB-Thermal (RGB-T) object tracking tasks, featuring the following key characteristics:
1. **Scale & Diversity**
- Contains 234,000 total frames, with sequences up to 8,000 frames
- Covers diverse scenarios and complex environmental conditions
- Currently the largest publicly available RGB-T dataset in the field
2. **Precise Multimodal Alignment**
- Strict spatiotemporal synchronization between RGB and thermal sequences
- Requires no pre/post-processing for direct usage
- Ensures cross-modal data consistency for reliable comparisons
3. **Granular Annotation System**
- Frame-level bounding box annotations
- Specially labeled occlusion levels for tracked objects
- Enables occlusion-sensitive analysis and robustness evaluation
4. **Core Innovations**
- Breaks scale limitations of existing datasets for comprehensive evaluation
- First benchmark with pixel-level multimodal alignment accuracy
- Introduces quantitative occlusion analysis as a new dimension
- Provides standardized validation for multimodal fusion algorithms
The dataset addresses longstanding evaluation challenges in RGB-T tracking by offering:
- Precisely aligned cross-modal data streams
- Fine-grained occlusion annotations
- Large-scale sample capacity
It enables in-depth research on:
- Effectiveness verification of multimodal feature fusion
- Sustained performance assessment for long-term tracking
- Algorithm robustness testing under varying occlusion levels
- Benchmark comparisons for cross-modal representation learning
As the first comprehensive RGB-T tracking benchmark, it establishes new research paradigms and reliability validation standards, significantly advancing visible-thermal fusion tracking technologies.
This dataset is a large-scale video benchmark constructed for RGB-Thermal (RGB-T) object tracking tasks, featuring the following key characteristics:
1. **Scale & Diversity**
- Contains 234,000 total frames, with sequences up to 8,000 frames
- Covers diverse scenarios and complex environmental conditions
- Currently the largest publicly available RGB-T dataset in the field
2. **Precise Multimodal Alignment**
- Strict spatiotemporal synchronization between RGB and thermal sequences
- Requires no pre/post-processing for direct usage
- Ensures cross-modal data consistency for reliable comparisons
3. **Granular Annotation System**
- Frame-level bounding box annotations
- Specially labeled occlusion levels for tracked objects
- Enables occlusion-sensitive analysis and robustness evaluation
4. **Core Innovations**
- Breaks scale limitations of existing datasets for comprehensive evaluation
- First benchmark with pixel-level multimodal alignment accuracy
- Introduces quantitative occlusion analysis as a new dimension
- Provides standardized validation for multimodal fusion algorithms
The dataset addresses longstanding evaluation challenges in RGB-T tracking by offering:
- Precisely aligned cross-modal data streams
- Fine-grained occlusion annotations
- Large-scale sample capacity
It enables in-depth research on:
- Effectiveness verification of multimodal feature fusion
- Sustained performance assessment for long-term tracking
- Algorithm robustness testing under varying occlusion levels
- Benchmark comparisons for cross-modal representation learning
As the first comprehensive RGB-T tracking benchmark, it establishes new research paradigms and reliability validation standards, significantly advancing visible-thermal fusion tracking technologies.
