YouTube-GDD: A challenging gun detection dataset with rich contextual information [arXiv] [Project Page]

Overview

To promote the development of security, this work presents a new challenging dataset called YouTube Gun Detection Dataset (YouTube-GDD). Our dataset is collected from 343 high-definition YouTube videos and contains 5000 well-chosen images, in which 16064 instances of gun and 9046 instances of person are annotated. Compared to other datasets, YouTube-GDD is "dynamic", containing rich contextual information and recording shape changes of the gun during shooting. To build a baseline for gun detection, we evaluate YOLOv5 on YouTube-GDD and analyze the influence of additional related annotated information on gun detection.

Dates

• [x] Release Training and validation sets. [2022-04]
• [x] Release test images. [2022-04]
• [ ] Open the evaluation server to the public. [to be confirmed]
• [ ] Augment dataset volumn to the level of ten thousand. [to be confirmed]

Description

1. All images are captured from YouTube videos.

2. All annotations are labeled in YOLO format with labelImg.

3. YouTube-GDD contains two categories, namely "person" and "gun", corresponding to category ids 0 and 1, respectively.

4. The name format of each image file and the corresponding label file is set as "YouTube id_original frame rate_split frame rate_ID".

Statistics

Firstly, we split the entire dataset into 10 nonoverlapping folds by filename, each containing 500 images. Secondly, we compute the ratio of different scales in the entire dataset as the probability distribution, and then compute the scale distribution of each fold. The two folds with the lowest JS divergence are chosen as test set and validation set, i.e., fold7 is chosen as the test set and fold6 is chosen as the validation set while the rest folds are adopted as the training set.

Table Note: Frames captured from the same video may be assigned into two adjacent folds, causing the video to be repeatedly counted.

Construct YouTube-GDD from Source Videos

[Update 18th April] We thank a2515919 who is also working on the dataset and willing to share the pre-processed images: Google Drive Link.

Here, three scripts are provided for constructing YouTube-GDD from source videos step by step.

• Download videos.

cd /path/to/YouTube-GDD/
python ./tools/download.py --videolist ./configs/videolist.txt  --videopath /path/to/videos

• Extract frames.

cd /path/to/YouTube-GDD/
python ./tools/extract.py  --videopath /path/to/videos  --framepath /path/to/frames

• Select images.

cd /path/to/YouTube-GDD/
python ./tools/select.py  --imagelist  ./configs/imagelist.npy  --framepath /path/to/frames --imagepath /path/to/images

After collecting images, unzip labels.zip to the parent root of imagepath and the expected dataset structure should be organized as follows, which also meets the dataset structure requirement of YOLOv5.

YouTube-GDD/
  images/
      train/
      val/
      test/
  labels/
      train/
      val/

Baseline

Table Note: TL means Transfer Learning and AoP means Annotations of Person.

Contact

If you have any general question, feel free to email us at [email protected]. If you have dataset-related or implementation-related questions, please feel free to send emails to us or open an issue in this codebase (We recommend that you open an issue in this codebase, because your questions may help others).

Citation

If you find our work inspiring or use our dataset in your research, please cite our work.

@article{gu2022youtube-gdd,
  title={YouTube-GDD: A challenging gun detection dataset with rich contextual information},
  author={Gu Yongxiang and Liao Xingbin and Qin Xiaolin},
  journal={arXiv preprint arXiv:2203.04129},
  year={2022}
}

Thanks

We thank Lab students, namely Mingfei Li, Jingyang Shan, Qianlei Wang, Siqi Zhang, Xu Liao, Yuncong Peng, Gang Luo, Xin Lan, Boyi Fu and Yangge Qian, for their suggestions about improving the YouTube-GDD dataset.