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Stereo Object Proposals
Shao Huang   Weiqiang Wang   Shengfeng He   Rynson W.H. Lau
University of Chinese Academy of Sciences
South China University of Technology
City University of Hong Kong                        
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## Dataset Introduction
This file describes the StereoObj dataset proposed by ¡°Stereo Object Proposals¡±, which is published in IEEE Transactions on Image Processing. The StereoObj dataset consists of both indoor and outdoor scenes to evaluate the performance of object proposals and to facilitate further object-related applications. The dataset comprises of 400 stereo image pairs, which are taken from different scenes including beaches, cities, streets, buildings, sculptures, sports, rooms and malls. These images contain different challenging factors, e.g., objects occlusion, persons stand shoulder to shoulder, cluttered background, tiny and far away objects, and objects with large depth variation. The locations of objects are labeled manually, and each image contains at least one object.

## Dataset Structure
The folder structure of the StereoObj dataset is as follows:
1. ¡°Left¡±       	¨C the folder of left-view images with .jpg format;
2. ¡°Right¡±      	¨C the folder of right-view images with .jpg format;
3. ¡°Depth¡±      	¨C the folder of generated depth maps with .png format;
4. ¡°GT_results¡± 	¨C the folder of images with ground truth bounding boxes in red color;
5. ¡°Annotations¡±	¨C the folder of annotation files with .txt format. Each annotation file corresponds to an image pair, and the number of rows denotes the number of labeled objects. Each row is formulated as ¡®66, 116, 279, 490¡¯ and denotes the location of the bounding box: X-axis of upper left corner (66), Y-axis of upper left corner (116), X-axis of bottom right corner (279), Y-axis of bottom right corner (490).

## Evaluation
Detection rate vs. number of proposals at different intersection-over-union (IoU) thresholds (e.g., 0.5, 0.6, 0.7, 0.9) is used as the evaluation metric.

## Citation
If you use the StereoObj dataset in your research, please cite this publication:
@article{huang2016stereo,
  title={Stereo Object Proposals},
  author={Huang, Shao and Wang, Weiqiang and He, Shengfeng and Lau, Rynson WH},
  journal={IEEE Transactions on Image Processing},
  year={2016},
  publisher={IEEE}
}
