Datasets:
Modalities:
Image
Formats:
parquet
Size:
100K - 1M
Tags:
person-reid
background-modification
segmentation
reid
ReIdentification
person-reidentification
File size: 4,790 Bytes
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---
dataset_info:
features:
- name: image
dtype: image
- name: pid
dtype: int64
- name: camid
dtype: int64
splits:
- name: bounding_box_train
num_bytes: 2066133251.5
num_examples: 142252
- name: query
num_bytes: 544940019.0
num_examples: 37048
- name: bounding_box_test
num_bytes: 2462488224.875
num_examples: 173481
download_size: 5078952368
dataset_size: 5073561495.375
tags:
- person-reid
- background-modification
- segmentation
- person-reid
- reid
- ReIdentification
- person-reidentification
- feature-extraction
---
# Dataset Card for "Market1501-Background-Modified"
## Dataset Summary
The **Market1501-Background-Modified** dataset is a variation of the original Market1501 dataset. It focuses on reducing the influence of background information by replacing the backgrounds in the images with solid colors, noise patterns, or other simplified alternatives. This dataset is designed for person re-identification (ReID) tasks, ensuring models learn person-specific features while ignoring background variations.
## Key Features
- **Modified Backgrounds**: Solid colors (e.g., red, blue, black) and noise patterns replace the original image backgrounds.
- **Preserves Original Structure**: Retains the original Market1501 structure with training, query, and gallery splits.
- **Segmentation Masks**: Utilizes segmentation masks to accurately isolate the person from the background.
## Dataset Structure
### Features
- **image**: Modified image file.
- **pid**: A unique identifier for each person.
- **camid**: Camera ID indicating the source camera.
### Splits
| Split | Num Examples | Size (bytes) | Description |
|--------------------|--------------|--------------|------------------------------------------------|
| bounding_box_train | 142,252 | 22,824,980 | Training set with modified backgrounds. |
| query | 37,048 | 5,463,160 | Query set for testing person re-identification.|
| bounding_box_test | 173,481 | 27,663,038 | Gallery set for evaluation against query images.|
### Dataset Statistics
- **Download Size**: 10,177,253 bytes
- **Dataset Size**: 55,951,178 bytes
- **Version**: 1.0
---
## Future Scope
The Market1501-Background-Modified dataset lays the groundwork for further advancements in person re-identification research. Below are several future directions to enhance the dataset and extend its applications:
1. **Inpainting-Based Background Replacement**:
- Instead of solid colors or noise, leverage advanced inpainting techniques to generate natural, contextually consistent backgrounds.
- Example: Use GANs or diffusion models to create realistic, yet non-informative, synthetic backgrounds.
2. **Dynamic Background Variations**:
- Introduce environment-consistent dynamic backgrounds (e.g., urban, indoor, or outdoor scenes) to simulate real-world conditions.
- Helps test the robustness of ReID models under diverse lighting and background scenarios.
3. **Dataset Expansion**:
- Apply similar segmentation and background modifications to other ReID datasets such as DukeMTMC-reID, CUHK03, or MSMT17.
- Enables cross-dataset analysis and domain adaptation studies.
4. **Motion Blur and Occlusion Simulation**:
- Add augmentations such as motion blur, partial occlusion, or shadow effects to create a more realistic dataset.
- Improves model performance in practical, challenging scenarios.
5. **Multi-Person Scenes**:
- Extend the dataset to include images with multiple persons in the scene.
- Encourages models to focus on the target individual, ignoring distractors.
6. **Multi-Modal Backgrounds**:
- Explore backgrounds that include texture-based cues, like pavement patterns or interior walls, to study their impact on ReID performance.
7. **Fine-Grained Metadata**:
- Incorporate additional metadata, such as time of day, season, or background type (solid color, noise, or inpainted), for detailed analysis.
---
## Dataset Usage
This dataset can be used for:
- Training person re-identification models.
- Evaluating models' ability to focus on person-specific features.
- Studying the impact of background modification on ReID performance.
## Citation
If you use this dataset, please cite both the original Market1501 dataset and this modified version:
```bibtex
@article{Market1501Modified,
author = {Deepankar Sharma},
title = {Market1501-Background-Modified: A Dataset for Person Re-identification with Reduced Background Influence},
year = {2025},
url = {https://huggingface.co/datasets/ideepankarsharma2003/Market1501-Background-Modified},
note = {Based on the original Market1501 dataset.}
}
```
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