TJU-DHD dataset (object detection and pedestrian detection)

This is the official website for "TJU-DHD: A Diverse High-Resolution Dataset for Object Detection (TIP2020)", which is a newly built high-resolution dataset for object detection and pedestrian detection.

• 115k+ images and 700k+ instances
• Scenes: traffic and campus, Tasks: object detection and pedestrian detection
• High resolution: image resolution of at least 1624x1200 pixels, the object height from 11 pixels to 4152 pixels.
• Diversity: A large variance in appearance, scale, illumination, season, and weather
• Cross-scene evaluation and same-scene evaluation on pedestrian detection
• If you are interested in pedestrian detection, please refer to our IEEE T-PAMI paper or our github project.
• Learderboard in Paperswithcode: TJU-Ped-campus, TJU-Ped-traffic

Table of Contents

1. Introduction
2. Object detection dataset
   2.1 TJU-DHD-traffic
   2.2 TJU-DHD-campus
3. Pedestrian detection dataset
   3.1 TJU-Ped-traffic
   3.2 TJU-Ped-campus
4. Benchmark
   4.1 TJU-DHD-traffic
   4.2 TJU-DHD-campus
   4.3 TJU-DHD-pedestrian
5. Citation
6. Evaluation on the test set
7. Contact

1. Introduction

Vehicles, pedestrians, and riders are the most important and interesting objects in the perception modules of self-driving vehicles and video surveillance. However, the state-of-the-art performance of detecting such important objects (esp. small objects) is far from satisfying the demand of the practical systems. Large-scale, rich-diversity, and high-resolution vehicle and pedestrian datasets play an important role in developing better object detection methods to satisfy the demand. Existing public large-scale datasets such as MS COCO collected from websites do not focus on these specific scenarios. Moreover, the popular datasets (e.g., KITTI and Citypersons) collected from these specific scenarios are limited in the number of images and instances, the resolution, and the diversity in seasons, weathers, and illuminations. To attempt to solve the problem, in this paper, we build a diverse high-resolution dataset (called TJU-DHD). The dataset contains 115,354 high-resolution images (52% images have a resolution of 1624x1200 pixels and 48% images have a resolution of at least 2,560x1,440 pixels) and 709,330 labeled objects in total with a large variance in scale and appearance. Meanwhile, the dataset has a rich diversity in season variance, illumination variance, and weather variance. Based on this object dataset, a new diverse pedestrian dataset is further built. With the four different detectors (i.e., the one-stage RetinaNet, anchor-free FCOS, two-stage FPN, and Cascade R-CNN), experiments about object detection and pedestrian detection are conducted. We hope that the newly built dataset can help promote the research on object detection and pedestrian detection in these two scenes.

2. Object detection dataset

name	DHD-traffic (#images)	DHD-traffic (#instances)	DHD-campus (#images)	DHD-campus (#instances)
training	45,266	239,980	39,727	267,445
validation	5,000	30,679	5,204	41,620
test	10,000	60,963	10,157	68,643
total	60,266	331,622	55,088	377,708

2.1 TJU-DHD-traffic

• training & validation set:
  • images: OneDrive / BaiduNetDisk (code: biit)
    • md5: c5de70827bde72a909a14d91be40ecc7
  • annotations: OneDrive / BaiduNetDisk (code: biit)
    • md5: 4bb78bbe78166f8904e46a691e6814c4
• test set:
  • images: OneDrive / BaiduNetDisk (code: biit)
    • md5: 74656dde79a4d5e856e31dfc1be30cbe
  • imageinfo: OneDrive / BaiduNetDisk (code: biit)
    • md5: 458bf63ca74992c3145570cbc62f65fe
• evaluation tools: cocoapi

2.2 TJU-DHD-campus

The training imageset is too large, thus is ziped as a 4-part archive. After downloading all four parts, you can open the .zip.001 using your favorite zip file extractor. On Linux, the multi-part archive can be also unzipped by

cat dhd_campus_train_images.zip.* > dhd_campus_train_images.zip
unzip dhd_campus_train_images.zip -d /path/to/your/folder

• training & validation set:
  • training images-1: OneDrive / BaiduNetDisk (code: biit)
    • md5: 1c781e3aab251a19cc8c304ec854b060
  • training images-2: OneDrive / BaiduNetDisk (code: biit)
    • md5: 502c25eb707718c3e99f4679f53b0fda
  • training images-3: OneDrive / BaiduNetDisk (code: biit)
    • md5: fc8ca78881eb94c3aa5d8d4ee8db5096
  • training images-4: OneDrive / BaiduNetDisk (code: biit)
    • md5: a3b33b17f0c89e57180bc174332ee764
  • validation images: OneDrive / BaiduNetDisk (code: biit)
    • md5: 7c8d32bbe38c4231121235965cc1c778
  • annotations: OneDrive / BaiduNetDisk (code: biit)
    • md5: 3d2301befe1fca63f4979454cf228c61
• test set:
  • images: OneDrive / BaiduNetDisk (code: biit)
    • md5: d0ada536ccb5851b42b656847d5b1e3b
  • imageinfo: OneDrive / BaiduNetDisk (code: biit)
    • md5: c3015863858740561944110aa9ce9b23
• evaluation tools: cocoapi

3. Pedestrian detection dataset

name	Ped-traffic (#images)	Ped-traffic (#instances)	Ped-campus (#images)	Ped-campus (#instances)
training	13,858	27,650	39,727	234,455
validation	2,136	5,244	5,204	36,161
test	4,344	10,724	10,157	59,007
total	20,338	43,618	55,088	329,623

3.1 TJU-Ped-traffic

(Note that the images are same as those in the TJU-DHD-traffic)

• training & validation set:
  • images: OneDrive / BaiduNetDisk (code: biit)
    • md5: c5de70827bde72a909a14d91be40ecc7
  • annotations: OneDrive / BaiduNetDisk (code: biit)
    • md5: 80c434f14283fa169a6fa90b6115c412
• test set:
  • images: OneDrive / BaiduNetDisk (code: biit)
    • md5: 74656dde79a4d5e856e31dfc1be30cbe
  • imageinfo: OneDrive / BaiduNetDisk (code: biit)
    • md5: e60c660de79c70a00795fde5e40fe28e
• evaluation tools: Citypersons API

3.2 TJU-Ped-campus

(Note that the images are same as those in the TJU-DHD-campus)

• training & validation set:
  • training images-1: OneDrive / BaiduNetDisk (code: biit)
    • md5: 1c781e3aab251a19cc8c304ec854b060
  • training images-2: OneDrive / BaiduNetDisk (code: biit)
    • md5: 502c25eb707718c3e99f4679f53b0fda
  • training images-3: OneDrive / BaiduNetDisk (code: biit)
    • md5: fc8ca78881eb94c3aa5d8d4ee8db5096
  • training images-4: OneDrive / BaiduNetDisk (code: biit)
    • md5: a3b33b17f0c89e57180bc174332ee764
  • validation images: OneDrive / BaiduNetDisk (code: biit)
    • md5: 7c8d32bbe38c4231121235965cc1c778
  • annotations: OneDrive / BaiduNetDisk (code: biit)
    • md5: 0c3af32827d3694656555630fe345ca0
• test set:
  • images: OneDrive / BaiduNetDisk (code: biit)
    • md5: d0ada536ccb5851b42b656847d5b1e3b
  • imageinfo: OneDrive / BaiduNetDisk (code: biit)
    • md5: 0dd0130714017255bea52d85f014f6af
• evaluation tools: Citypersons API

4. Benchmark

4.1 TJU-DHD-traffic

• Results on validation

  method	backbone	input size	AP	[email protected]	[email protected]	AP_s	AP_m	AP_l
  RetinaNet	ResNet50	1333x800	53.5	80.9	60.0	24.0	50.5	68.0
  FCOS	ResNet50	1333x800	53.8	80.0	60.1	24.6	50.6	68.8
  FPN	ResNet50	1333x800	55.4	83.4	63.0	30.4	52.2	68.2
  Cascade RCNN	ResNet50	1333x800	57.9	82.7	66.6	32.6	54.4	71.4

4.2 TJU-DHD-campus

• Results on validation

  method	backbone	input size	AP	[email protected]	[email protected]	AP_t	AP_s	AP_l	AP_l
  RetinaNet	ResNet50	1333x800	48.4	79.3	52.4	4.7	27.3	56.2	73.8
  FCOS	ResNet50	1333x800	49.3	73.8	53.8	5.6	29.6	55.9	74.3
  FPN	ResNet50	1333x800	52.4	77.5	58.4	8.5	37.4	58.6	74.9
  Cascade RCNN	ResNet50	1333x800	55.1	77.6	60.9	10.8	40.1	61.2	78.8

4.3 TJU-DHD-pedestrian

• TJU-Ped-campus

Method	publication	R	RS	HO	R+HO	A	link
RetinaNet	ICCV2017	34.73	82.99	71.31	42.26	44.34	Paper
FCOS	ICCV2019	31.89	69.04	81.28	39.38	41.62	Paper
FPN	ICCV2017	27.92	67.52	73.14	35.67	38.08	Paper
CrowdDet	CVPR2020	25.73	-	66.38	33.63	35.90	Paper
EGCL	IEEE TIP2023	24.84	-	65.27	32.39	34.87	Paper
DeFCN	CVPR2021	32.1	62.7	72.7	39.9	42.1	Paper
OPL	CVPR2023	31.5	61.7	72.4	39.3	41.5	Paper
MTOM	WACV2023	21.8	37.04	57.08	-	-	Paper

• TJU-Ped-traffic

Method	publication	R	RS	HO	R+HO	A	link
RetinaNet	ICCV2017	23.89	37.92	61.60	28.45	41.40	Paper
FCOS	ICCV2019	24.35	37.40	63.73	28.86	40.02	Paper
FPN	ICCV2017	22.30	35.19	60.30	26.71	37.78	Paper
CrowdDet	CVPR2020	20.82	-	61.22	25.28	36.94	Paper
EGCL	IEEE TIP2023	19.73	-	60.05	24.19	35.76	Paper
DeFCN	CVPR2021	24.2	29.1	62.8	29.0	39.7	Paper
Pedestron	CVPR2021	18.9	24.0	56.3	-	-	Paper
OPL	CVPR2023	23.4	28.8	62.7	28.0	38.7	Paper
LSFM	CVPR2023	18.7	24.9	56.2	-	-	Paper
MTOM	WACV2023	17.4	24.7	52.68	-	-	Paper

• Cross-scene evaluation

  method	R/R+HO (TJU-Ped-campus -> traffic)	R/R+HO (TJU-Ped-traffic -> campus)
  FPN	30.62 / 33.89	42.08 / 50.55

5. Citation

If this project help your research, please consider to cite our works.

@article{Pang_DHD_TIP_2020,
         author = {Yanwei Pang and Jiale Cao and Yazhao Li and Jin Xie and Hanqing Sun and Jinfeng Gong},
         title = {TJU-DHD: A Diverse High-Resolution Dataset for Object Detection},
         journal = {IEEE Transactions on Image Processing},
         year = 2021
        }

@article{Cao_PDR_TPAMI_2020,
         author = {Jiale Cao and Yanwei Pang and Jin Xie and Fahad Shahbaz Khan and Ling Shao},
         title = {From Handcrafted to Deep Features for Pedestrian Detection: A Survey},
         journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
         year = 2022
        }

6. Evaluation on the test set

Ablation studies can be conducted on the validation set. If you would like to evaluate your model on the test set, you can send us (connor#tju.edu.cn, replace # with @) your detection results in the json format.

7. Contact

If you have any questions or want to add your results, please feel free to contact us.