NWPU Campus dataset

Congqi Cao   Yue Lu   Peng Wang   Yanning Zhang  

National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology (ASGO), School of Computer Science, Northwestern Polytechnical University, China

Paper: A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation

Extension version: Scene-Dependent Prediction in Latent Space for Video Anomaly Detection and Anticipation

NWPU Campus is a dataset proposed for (semi-supervised) video anomaly detection (VAD) and video anomaly anticipation (VAA). It is currently the largest and most complex dataset in its field with 43 scenes, 28 classes of anomalous events and 16 hours of videos. Especially, it contains scene-dependent anomalies, which means an event may be normal in one scene but abnormal in another.

Introduction

A number of normal and anomalous events in different scenes are displayed in Figure 1. All the 28 classes of anomalous events are listed in Table 1.
Table 2 and Figure 2 show the statistics of the dataset, including durations and distributions.

Samples

Figure 1. Samples from the proposed NWPU Campus dataset. The samples in the first column are normal events, while the others are different types of anomalous events.

Classes of anomalies

Statistics

Figure 2. The distributions of training and testing videos according to duration (a),
and abnormal testing videos according to the percentage of abnormal frames in each video (b).

Download the dataset

Terms & Conditions of use

The NWPU Campus dataset is released for academic research only, and is free to researchers from educational or research institutes for non-commercial purposes. The use of the NWPU Campus dataset is governed by the following terms and conditions:

• Without the expressed permission of the ASGO Lab, any of the following will be considered illegal: redistribution, derivation or generation of a new dataset from this dataset, and commercial usage of any of these datasets in any way or form, either partially or in its entirety.
• For the sake of privacy, images of all subjects in any of these datasets are only allowed for the demonstration in academic publications and presentations.
• All users of NWPU Campus dataset agree to indemnify, defend and hold harmless, the ASGO Lab and its officers, employees, and agents, individually and collectively, from any and all losses, expenses, and damages.

Download

There are 305 training videos and 242 testing videos in the NWPU Campus dataset. All of the videos amount to 76.6 GB in disk space.

Download from BaiduYun (code: il54) [101 downloads] or Google Drive

ShanghaiTech-sd

The training videos and testing videos of the ShanghaiTech-sd datasets reorganized by us are shown in Table 3. In the training videos, the scene "01" contains "cycling" events picked from the testing set of the original ShanghaiTech dataset, while cycling is not included in other scenes. All the scenes in the testing set contain cycling. However, cycling in the scene "01" is a normal behavior, while it is an abnormal behavior in other scenes.

Download

Download the Shanghaitech-sd from BaiduYun (code: il54) [21 downloads]

Code

Github

Citation

			@InProceedings{Cao_2023_CVPR,
	    		author    = {Cao, Congqi and Lu, Yue and Wang, Peng and Zhang, Yanning},
	    		title     = {A New Comprehensive Benchmark for 
				     Semi-Supervised Video Anomaly Detection and Anticipation},
	    		booktitle = {Proceedings of the IEEE/CVF Conference on 
				     Computer Vision and Pattern Recognition (CVPR)},
	    		month     = {June},
	    		year      = {2023},
	    		pages     = {20392-20401}
			}
			

			@article{cao2024scene,
  			title={Scene-Dependent Prediction in Latent Space for 
			       Video Anomaly Detection and Anticipation},
  			author={Cao, Congqi and Zhang, Hanwen and Lu, Yue and Wang, Peng and Zhang, Yanning},
  			journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  			year={2024},
  			publisher={IEEE}
			}
			

Contact

For further questions and suggestions, please contact Yue Lu (zugexiaodui@mail.nwpu.edu.cn).