PKU-VD Dataset

INTRODUCTION

The PKU-VD datasets including VD1 and VD2 are constructed by National Engineering Laboratory for Video Technology (NELVT), Peking University, sponsored by the National Basic Research Program of China and Chinese National Natural Science Foundation. We construct two large-scale vehicle datasets1 (i.e., VD1 and VD2) based on real-world unconstrained scenes from two cities respectively. The images in VD1 are obtained from high resolution traffic cameras, and images in VD2 are captured from surveillance videos. We perform vehicle detection on the raw data to make sure that each image only contains one vehicle. The region of plate number has been covered by black color due to privacy protection.

We provide diverse attribute annotations for each image in both two datasets, including identity number, precise vehicle model and vehicle color. Specifically, identity number (ID) is unique and all images belong to the same vehicle have the same ID (we make sure that there are at least two images in the dataset for each vehicle ID). We provide the most precise model type with detailed vehicle type and different produced years. For example, Audi-A6L-2012&2015, Audi-A6-2004, Audi-A4-2006&2008 and Audi-A4-2004&2005 are four different vehicle models in our datasets. As for color information, 11 common colors are annotated in our datasets. We carefully check all annotations to ensure the consistency of labels so that all the images belonging to the same vehicle ID are annotated with the same vehicle model and color. 

VD1: There are total 1,097,649 images in the dataset. We label 1,232 vehicle models and 11 colors.

VD2: There are total 807,260 images in the dataset. We label 1,112 vehicle models and 11 colors.
LICENSE 

The PKU-VD datasets are now partly made available for the academic purpose only on a case-by-case basis. The NELVT at Peking University is serving as the technical agent for distribution of the dataset and reserves the copyright of all the images in the dataset. Any researcher who requests the PKUVehicleID dataset must sign this agreement and thereby agrees to observe the restrictions listed in this document.

  • The images and the corresponding annotation results for download are part of PKU-VD datasets.
  • The images and the corresponding annotation results can only be used for ACADEMIC PURPOSES. NO COMERCIAL USE is allowed.
  • Copyright © National Engineering Laboratory for Video Technology (NELVT) and Institute of Digital Media, Peking University (PKU-IDM). All rights reserved.

All publications using PKU-VD dataset should cite the paper below:

@inproceedings{yan2017exploiting,
 title={Exploiting Multi-Grain Ranking Constraints for Precisely Searching Visually-Similar Vehicles},
 author={Yan, Ke and Tian, Yonghong and Wang, Yaowei and Zeng, Wei and Huang, Tiejun},
 booktitle={Proceedings of the IEEE International Conference on Computer Vision},
 pages={562--570},
 year={2017}
}

DOWNLOAD

You can download the agreement (pdf) from here. Please make sure that a permanent/long-term responsible person (e.g., professor, PI) fills in the agreement with a handwriting signature. After filling it, please send the electrical version to our Email: pkuml at pku.edu.cn (Subject: PKU-VD-Agreement)

Please send it through an academic or institute email-addresses such as xxx at xxx.edu.xx. Requests from free email addresses (outlook, gmail, qq etc) will be kindly refused.

After confirming your information, we will send the download link and password to you via Email. You need to follow the agreement.

Usually we will reply in a week. But sometimes the mail does not arrive and display successfully for some unknown reason. If this happened, please change the content or title and try sending again.

DRDL

This page includes some resource of our paper “Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles”.

Since we are not allowed to release the complete code due to some confidential protocols, we provide only the core part and the model’s prototxt files(for both training and testing) here.

  • You can find the source code of “coupled clusters loss”, “triplet loss”, “l2 normalization” and all other related caffe code here. Notice that you can not feed multiple labels to a normal data layer in the original Caffe. Thus, we modified “MemoryDataLayer” to support it. For instance, if you want to feed 3 different labels(label1, label2, label3) into the MemoryDataLayer, please edit your data layer in the prototxt like.

layer {
	name: "data"
	type: "MemoryData"
	top: "data"
	top: "label1"
	top: "label2"
	top: "label3"
	include {
		phase: TRAIN
	}
	memory_data_param {
		num_tasks: 3
		batch_size: 128
		channels: 3
		height: 224
		width: 224
	}
}

You can then feed the input data in Python like

x = np.zeros((128, 3, 224, 224), dtype=np.float32)
y = np.zeros((3, 128), dtype=np.float32)
solver.net.set_input_arrays(X, Y)
  • We also release our model’s prototxt files for both training and testing. You can download it here.
  • The VehicleID dataset can be find here.

PKU VehicleID

INTRODUCTION

The PKU VehicleID dataset is constructed by National Engineering Laboratory for Video Technology (NELVT), Peking University, sponsored by the National Basic Research Program of China and Chinese National Natural Science Foundation.

The “VehicleID” dataset contains data captured during daytime by multiple real-world surveillance cameras distributed in a small city in China. There are 26267 vehicles(221763 images in total) in the entire dataset. Each image is attached with an id label corresponding to its identity in real world. In addition, we manually labeled 10319 vehicles(90196 images in total) of their vehicle model information(i.e.“MINI-cooper”, “Audi A6L” and “BWM 1 Series”).

Screen Shot 2016-08-01 at 12.46.07 PM

The PKU VehicleID dataset is now partly made available for the academic purpose only on a case-by-case basis. The NELVT at Peking University is serving as the technical agent for distribution of the dataset and reserves the copyright of all the images in the dataset. Any researcher who requests the PKUVehicleID dataset must sign this agreement and thereby agrees to observe the restrictions listed in this document.

LICENSE

  • The images and the corresponding annotation results for download are part of PKU VehicleID dataset.
  • The images and the corresponding annotation results can only be used for ACADEMIC PURPOSES. NO COMERCIAL USE is allowed.
  • Copyright © National Engineering Laboratory for Video Technology (NELVT) and Institute of Digital Media, Peking University (PKU-IDM). All rights reserved.

All publications using PKU VehicleID dataset should cite the paper below:

@inproceedings{liu2016deep,
  title={Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles},
  author={Liu, Hongye and Tian, Yonghong and Wang, Yaowei and Pang, Lu and Huang, Tiejun},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2167--2175},
  year={2016}
}

DOWNLOAD

You can download the agreement (pdf) from here. After filling it, please send the electrical version to our Email: pkuml at pku.edu.cn (Subject: PKU-VehicleID-Agreement)  .

Please send it through an academic or institute email-addresses such as xxx at xxx.edu.xx. Requests from free email addresses (outlook, gmail, qq etc) will be kindly refused.

After confirming your information, we will send the download link and password to you via Email. You need to follow the agreement.

Usually we will reply in a week. But sometimes the mail does not arrive and display successfully for some unknown reason. If this happened, please change the content or title and try sending again.

Code

Papers

Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks

  • Wei Fang, Zhaofei Yu, Yanqi Chen, Timothée Masquelier, Tiejun Huang and Yonghong Tian*
  • ICCV 2021
  • For an introduction to the paper, see the README.md. [ENGLISH/CHINESE]

[PDF] [Code]

 

Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles

  • Hongye Liu, Yonghong Tian*, Yaowei Wang*, Lu Pang, Tiejun Huang
  • CVPR 2016

[PDF] [Code]

 

Unsupervised Cross-Dataset Transfer Learning for Person Re-identification

  • Peixi Peng, Tao Xiang, Yaowei Wang, Massimiliano Pontil, Shaogang Gong, Tiejun Huang, Yonghong Tian*
  • CVPR 2016

[PDF] [Code]

 

Learning Complementary Saliency Priors for Foreground Object Segmentation in Complex Scenes

  • Yonghong Tian, Jia Li, Shui Yu, Tiejun Huang
  • Int’l Journal of Computer Vision, 111(2), Jan 2015, 153-170. 10.1007/s11263-014-0737-1

[PDF] [Code]

 

Image Saliency Estimation via Random Walk Guided by Informativeness and Latent Signal Correlations

  • Jia Li, Shu Fang (first co-author), Yonghong Tian*, Tiejun Huang, and Xiaowu Chen
  • Signal Processing: Image Communication, (2015)

[PDF] [Code]

 

Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition

  • Yixiong Zou, Shanghang Zhang, Jianpeng Yu, Yonghong Tian*, José M. F. Moura
  • 29th ACM Int’l Conf. Multimedia. (2021)

[PDF] [Code] (Password: ko4q)

 

Projects

SpikingJelly

  • SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
  • The documentation of SpikingJelly is written in both English and Chinese: https://spikingjelly.readthedocs.io.
  • For an introduction to the project, see the README.md.

[OpenI] [Github]

PKU HumanID Dataset

The PKU HumanDI dataset is constructed by National Engineering Laboratory for Video Technology (NELVT), Peking University, sponsored by the National Basic Research Program of China and Chinese National Natural Science Foundation.

This dataset is composed of videos subjects crossing 11 cameras in a campus. It includes 6 high definition network cameras (Camera HD01, Camera HD02, Camera HD03, Camera HD04, Camera HD05, Camera HD06) and 7 normal network cameras (Camera BWBQ, Camera DCM, Camera WMHD, Camera XDMN, Camera YGLN, Camera YGLQ, Camera YTX). Some samples of the labeled results are shown below:

pku-humanid-dataset-1

HD Cameras (HD 01, HD 02, HD 04, HD 06)

pku-humanid-dataset-2

Normal Cameras (WMHD, DCM, YTX, YGLN)

The PKU HumanID dataset is now partly made available for the academic purpose only on a case-by-case basis. The NELVT at Peking University is serving as the technical agent for distribution of the dataset and reserves the copyright of all the videos in the dataset. Any researcher who requests the PKU HumanID dataset must sign this agreement and thereby agrees to observe the restrictions listed in this document.

LICENSE

  • The videos and the corresponding annotation results for download are part of PKU HumanID.
  • The videos and the corresponding annotation results can only be used for ACADEMIC PURPOSES. NO COMERCIAL USE is allowed.
  • Copyright © National Engineering Laboratory for Video Technology (NELVT) and Institute of Digital Media, Peking University (PKU-IDM). All rights reserved.

All publications using PKU HumanID dataset should cite the paper below:

  • Lan Wei, Yonghong Tian, Yaowei Wang, Tiejun Huan, Swiss-System Based Cascade Ranking for Gait-based Person Re-identification, 29th AAAI Conference on Artificial Intelligence, Austin Texas, USA, January, 2015

DOWNLOAD

You can download the agreement (pdf) by clicking the DOWNLOAD link.

After filling it, please send the electrical version to our Email: pkuml at pku.edu.cn (Subject: PKU-HumanID-Agreement)

After confirming your information, we will send the download link and password to you via Email. You need to follow the agreement.

Image saliency: From intrinsic to extrinsic context

An implementation of “Wang M, Konrad J, Ishwar P, Jing K, Rowley H (2011) Image saliency: From intrinsic to extrinsic context. CVPR, 2011.”

Re-implemented by Jia Li (jiali@buaa.edu.cn) and Shu Fang (sfang@pku.edu.cn).

Code folder: contains our implementation of (Wang et al. 2011) and the metrics for computing AUC, EOF and FS. More details can be found in our paper submitted to IJCV (J. Li et al. Measuring Visual Surprise Jointly from Intrinsic and Extrinsic Contexts for Image Saliency Estimation)

MIT1003 folder: the data used for testing (Wang et al. 2011). Subfolder “image” contains all the images from the dataset MIT1003, and subfolder “refImages” contains all the 20 most similar images retrieved from a large database with 31.2 million images.

Result folder: three subfolders, IES_intSal, IES_extSal and IES, contain the saliency maps from intrinsic context, extrinsic context and both contexts, respectively.

 

Code:
/mlg/download/code/wang11.zip
/mlg/download/code/wang11-codeResult.zip

PKU-RSD Dataset

We constructed this PKU-RSD (Regional Saliency Dataset) dataset that could capture spatiotemporal visual saliency for evaluating different video saliency models. This dataset contains 431 short videos, which cover various scenes (surveillance, ad, news, cartoon, movie etc.) and the corresponding annotation results of salient objects in sampled key frames manually labeled by 23 subjects. Some samples of the annotation results are shown below:

samples of RSD

LICENSE

  • The videos and the corresponding annotation results for download are part of PKU-RSD (Regional Saliency Dataset) dataset.
  • The videos and the corresponding annotation results can only be used for ACADEMIC PURPOSES. NO COMERCIAL USE is allowed.
  • Copyright © National Engineering Laboratory for Video Technology (NELVT) and Institute of Digital Media, Peking University (PKU-IDM). All rights reserved.

All publications using PKU-RSD should cite the paper below:

  • Jia Li, Yonghong Tian, Tiejun Huang, Wen Gao. A DATASET AND EVALUATION METHODOLOGY FOR VISUAL SALIENCY IN VIDEO. ICME 2009

DOWNLOAD
You can download the agreement (pdf) by clicking the DOWNLOAD link.
After filling it, please send the electrical version to our Email: pkuml at pku.edu.cn (Subject: PKU-RSD Agreement)
After confirming your information, we will send the download link and password to you via Email. You need to follow the agreement.

PKU-SVD-A DATASET

Here is several surveillance videos of the PKU-SVD-A Dataset for download. The archive consists of six SD576 and two 1200p sequences.

Resolution Video Name Frames Frame Rate
SD576(720×576) Bank 3000 30fps
Campus
Classover
Crossroad
Office
Overbridge
1200p(1600×1200) Intersection 1000 30fps
Mainroad

Bank Bank

Bank Campus

Bank Classover

Bank Crossroad

Bank Office

Bank Overbridge

Bank Intersection

Bank Mainroad

PKU-SVD-A

To evaluate the surveillance video coding performance, we constructed a large-scale dataset, called PKU-SVD-A, by collecting 73 videos with different resolutions (ranging from SD, 720p, 1600*1200, and 1920*1080) or at different weather and time conditions (e.g., dark, fog, rain,…). This dataset will be online publically available soon for the research usage.

LICENSE

  • The videos for download is part of PKU-SVD-A (Peking University Surveillance Video Dataset A).
  • The videos can only be used for ACADEMIC PURPOSES. NO COMERCIAL USE is allowed.
  • Copyright © National Engineering Laboratory for Video Technology (NELVT) and Institute of Digital Media, Peking University (PKU-IDM). All rights reserved.

All publications using PKU-SVD-A should cite the papers below:

  1. X. Zhang, Y. Tian, T. Huang, S. Dong, W. Gao, Optimizing the hierarchical prediction and coding in HEVC for surveillance and conference videos with background modeling, IEEE Trans. on Image Processing, 2014.
  2. X. Zhang, T. Huang, Y. Tian, W. Gao, Background Modeling Based Adaptive Prediction for Surveillance Video Coding, IEEE Trans. on Image Processing, 2014.
  3. W. Gao, Y. Tian, T. Huang, S. Ma, X. Zhang, IEEE 1857 Standard Empowering Smart Video Surveillance Systems, IEEE Intelligent Systems, 2013

Download

  • You can download the agreement(pdf) by clicking the DOWNLOAD link.
  • After filling it, please send the electrical version to our Email: pkuml at pku.edu.cn (Subject: PKU-SVD-A Agreement) .Please send it through an academic or institute email-addresses such as xxx at xxx.edu.xx. Requests from free email addresses (outlook, gmail, qq etc) will be kindly refused.

PKU-EAQA DATASET

To compare the performances of different enhancement algorithms, we constructed this PKU-EAQA (Peking University Enhancement Algorithm Quality Assessment) dataset. This dataset contains 300 images in bad visibility (in haze, low light, etc.), 1500 enhanced images generated by different enhancement algorithms and their subjective quality assessment results.

LICENSE

  • The images and the subjective quality assessment results for download are part of PKU-EAQA (Peking University Enhancement Algorithm Quality Assessment) dataset.
  • The images and the subjective quality assessment results can only be used for ACADEMIC PURPOSES. NO COMERCIAL USE is allowed.
  • Copyright © National Engineering Laboratory for Video Technology (NELVT) and Institute of Digital Media, Peking University (PKU-IDM). All rights reserved.

All publications using PKU-EAQA should cite the papers below:

  • Zhengying Chen, Tingting Jiang, Yonghong Tian, Quality Assessment for Comparing Image Enhancement Algorithms. To appear in IEEE Conference on Computer Vision and Pattern Recognition, 2014.

CVPR2014-Poster-PaperID1469
DOWNLOAD
You can download the agreement(pdf) by clicking the DOWNLOAD link.
After filling it, please send the electrical version to our Email: pkuml at pku.edu.cn (Subject: PKU-EAQA Agreement) After confirming your information, we will send the download link and password to you via Email. You need to follow the agreement.

PKU-VISUAL-OBJECTS DATASET

The PKU-VISUAL-OBJECTS dataset is constructed by National Engineering Laboratory for Video Technology (NELVT), Peking University, sponsored by the National Basic Research Program of China and Chinese National Natural Science Foundation.
the PKU-VISUAL-OBJECTS dataset is now partly made available for the academic purpose only on a case-by-case basis. The NELVT at Peking University is serving as the technical agent for distribution of the dataset and reserves the copyright of all the videos in the dataset. Any researcher who requests the PKU-VISUAL-OBJECTS dataset must sign this agreement and thereby agrees to observe the restrictions listed in this document. Failure to observe the restrictions will result in access being denied for the request of the future version of the PKU-VISUAL-OBJECTS dataset and being subject to civil damages in the case of publication of videos that have not been approved for release.

VIDEO MASKS

Resolution Sequence Frames Mask
1080p Kimono 238  kimono_1920x1080_24_mask238
ParkScene 238  ParkScene_1920x1080_24_mask238
BasketballDrive 499  BasketballDrive_1920x1080_50_mask499
720p Johnny 598  Johnny_1280x720_60_mask598
KristenAndSara 598  KristenAndSara_1280x720_60_mask598
FourPeople 598  FourPeople_1280x720_60_mask598

LICENSE

  • The videos for download is part of PKU-VISUAL-Objects Dataset.
  • The videos can only be used for ACADEMIC PURPOSES. For COMERCIAL USE, please contact us for authorization .
  • Copyright © National Engineering Laboratory for Video Technology (NELVT) and Institute of Digital Media, Peking University (PKU-IDM). All rights reserved.

All publications using PKU-VISUAL-OBJECTS Dataset should cite the paper below:

  • Tiejun Huang, Siwei Dong, Yonghong Tian, Representing Visual Objects in HEVC
    Coding Loop, IEEE Journal on Emerging and Selected Topics in Circuits and
    Systems, Volume 4, Issue 1, March 2014, 5-16. DOI:
    10.1109/JETCAS.2014.2298274.

Download

  • You can download the agreement(pdf) by clicking the DOWNLOAD link.
  • After filling it, please send the electrical version to our Email: pkuml at pku.edu.cn (Subject: PKU-VISUAL-OBJECTS Dataset Agreement)
  • The original videos (without masks) are part of HEVC/H.265 common test sequences, which can be downloaded from JCT-VC FTP (ftp://hevc@ftp.tnt.uni-hannover.de/testsequences).