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360VOT: Omnidirectional Visual Object Tracking

Homepage | Paper | Video | Benchmark Dataset

360VOT: A New Benchmark Dataset for Omnidirectional Visual Object Tracking
Huajian Huang, Yinzhe Xu, Yingshu Chen and Sai-Kit Yeung
The Hong Kong University of Science and Technology
In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV2023)

image

Introduction

The proposed 360VOT is the first benchmark dataset for omnidirectional visual object tracking. 360VOT contains 120 sequences with up to 113K high-resolution frames in equirectangular projection. It brings distinct challenges for tracking, e.g., crossing border (CB), large distortion (LD) and stitching artifact (SA). We explore new representations for visual object tracking and provide four types of unbiased ground truth, including bounding box (${\textcolor{#30bce3}{BBox}}$), rotated bounding box (${\textcolor{#79e0a7}{rBBox}}$), bounding field-of-view (${\textcolor{#e0a165}{rBBox}}$), rotated bounding field-of-view (${\textcolor{#e06b60}{rBFoV}}$). We propose new metrics for omnidirectional tracking evaluation, which measure the dual success rate and angle precision on the sphere. By releasing 360VOT, we believe that the new dataset, representations, metrics, and benchmark can encourage more research and application of omnidirectional visual object tracking in both computer vision and robotics.

Installation

To use the toolkit,

git clone https://github.com/HuajianUP/360VOT.git
cd 360VOT
pip install -r requirements.txt

Currently, the toolkit uses Rotated_IoU to calculate rBBox IoU for evaluation. When you seek to evaluate the tracking results in term of rBBox, you should install Rotated_IoU.

git submodule add https://github.com/lilanxiao/Rotated_IoU eval/Rotated_IoU

Usage

Evaluation on 360VOT dataset

Please use the following structure to store tracking results. The subfolders tracker_n/ contain tracking results (.txt files) of distinct methods on 120 sequences. The data format for different tracking representations in the txt file: BBox is [x1 y1 w h], rBBox is [cx cy w h rotation], BFoV and rBFoV are [clon clat fov_horizontal fov_vertical rotation]. All the angle values are in a degree manner, e.g., 'BFoV=[0 30 60 60 10]'.

results
├── traker_1
│   ├── 0000.txt
│   ├── ....
│   └── 0120.txt
├── ....
│
└── traker_n
    ├── 0000.txt
    ├── ....
    └── 0120.txt

For quick testing, you can download the benchmark results and unzip them in the folder benchmark/. If you have not downloaded the 360VOT dataset, you also need to download the Dataset. Then, you can evaluate the BFoV results using the command:

python scripts/eval_360VOT.py -f benchmark/360VOT-bfov-results -d PATH_TO_360VOT_DATASET
Command Line Arguments for eval_360VOT.py
Args Meaning
-d / --dataset_dir Path to 360VOT dataset.
-b / --bbox_dir Specify the path to the bbox results when you evaluate the results in bbox.
-rb / --rbbox_dir Specify the path to the rbbox results when you evaluate the results in rbbox.
-f / --bfov_dir Specify the path to the bfov results when you evaluate the results in bfov.
-rf / --rbbox_dir Specify the path to the rbfov results when you evaluate the results in rbfov.
-a / --attribute Specify the path to the 360VOT_attribute.xlsx, when you evaluate the results regarding different attributes.
-v / --show_video_level Print metrics in detail.
-p / --plot_curve Plot the curves of metrics.
-s / --save_path Specify the path to save the figure of metrics.

Command for visualizing the results and making a video:

# visualize the ground truth
python scripts/vis_result.py -d PATH_TO_DATASET -p PATH_TO_SAVE_VIDEOS [-ss VIDEO_OF_SPECIFIC_SEQUENCE]

# visualize the tracking results
python scripts/vis_result.py -d PATH_TO_DATASET -p PATH_TO_SAVE_VIDEOS -f PATH_TO_BFOV_RESULTS [-ss VIDEO_OF_SPECIFIC_SEQUENCE]

Command for checking parts of attributes of 360VOT dataset:

python scripts/check_360VOT_attribute.py --dir PATH_TO_DATASET [--excel PATH_TO_360VOT_attribute.xlsx]

Processing 360-degree image (equirectangular)

The toolkit contains an essential library for processing 360o images. The operations include:

crop_bfov: to extract the region of given (r)BFoV from the 360o image.

plot_bfov: to plot the region of given (r)BFoV on the 360o image.

Example

image

crop_bbox: to extract the region of given (r)BBox from the 360o image.

plot_bbox: to plot the region of given (r)BBox on the 360o image.

rot_image: to rotate the image by the pitch, yaw, or roll angle.

Example

image

localBbox2Bfov: convert the (r)BBox predictions on the extracted region to (r)BFoV regarding the original 360o image.

localBbox2Bbox: convert the (r)BBox predictions on the extracted region to (r)BBox regarding the original 360o image.

Example

image

mask2Bfov: estimate the (r)BFoV from the masked images.

mask2Bbox: estimate the (r)BBox from the masked images.

Example

image

For more examples, please refer to scripts/test_omni.py


We use a spherical camera model to formulate the relationship between the 2D image and the 3D camera coordinate system.
Illustration of coordinate system coordinate system

Image Credit: 360VO

Citation

If you use 360VOT and this toolkit for your research, please reference:

@InProceedings{huang360VOT,
   author    = {Huajian Huang, Yinzhe Xu, Yingshu Chen and Sai-Kit Yeung},
   title     = {360VOT: A New Benchmark Dataset for Omnidirectional Visual Object Tracking},
   booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
   month     = {October},
   year      = {2023},
   pages     = {}
}