Toolkits for MultiviewX dataset

@inproceedings{hou2020multiview,
  title={Multiview Detection with Feature Perspective Transformation},
  author={Hou, Yunzhong and Zheng, Liang and Gould, Stephen},
  booktitle={ECCV},
  year={2020}
}

Overview

The MultiviewX dataset is dedicated to multiview synthetic pedestrian detection. Using pedestrian models from PersonX, in Unity, we build a novel synthetic dataset MultiviewX. It follows the WILDTRACK dataset for set-up, annotation, and structure.

The MultiviewX dataset is generated on a 25 meter by 16 meter playground. It has 6 cameras that have overlapping field-of-view. The images in MultiviewX dataset are of high resolution, 1920x1080, and are synchronized. To fully exploit the complementary views, calibrations are also provided in MultiviewX dataset.

Downloads

Please refer to this link for download.

Toolkits for MultiviewX dataset.

This repo includes the toolkits and utilities for building MultiviewX dataset.

How to's

• download (from link) and copy the 2d/3d bbox annotations into /matchings.
• run the following command.

python run_all.py

• done.

Evaluation

For multiview pedestrian detection, MultiviewX follows the same evaluation scheme as Wildtrack with MODA, MODP, precission, and recall. Evaluation toolkit can be found here.

Leaderboards

Method	MODA	MODP	Precision	Recall
RCNN & clustering [cite]	18.7	46.4	63.5	43.9
DeepMCD [cite]	70.0	73.0	95.7	73.3
Deep-Occlusion [cite]	76.8	59.7	97.8	70.2
MVDet [cite] [code]	83.9	79.6	96.8	86.7