Introduction
The mission of the Quantitative Imaging Laboratory (QIL) group at the University of Houston has been to develop novel methods and systems for image and scene analysis that are capable of making human-like decisions. As part of this research, we are involved in an ongoing effort to collect a dataset of surveillance imagery. This dataset is meant to aid research efforts in the general area of developing, testing, and evaluating anomaly detection algorithms in surveillance videos. The University of Houston (henceforth, “UH”) owns the copyright of the collection of surveillance images labeled and serves as the source for this data.

Release of the Database
To advance the state-of-the-art in video anomaly detection, the dataset will be made available to researchers on a case-by-case basis. All requests for the ADOC are submitted to the Principal Investigator by the researcher or research unit (henceforth the “Licensee”). To receive a copy of the data, Licensee must agree to this document and agree to observe the restrictions listed below. In addition to other possible remedies, failure to observe these restrictions may result in revocation of permission to use the data as well as denial of access to additional databases distributed by UH. The database will be distributed over the Internet to licensees only. There will be no charge for data made available and downloaded via the Internet.

Consent
The researcher(s) agrees to the following restrictions on the ADOC Dataset:
Redistribution: Without prior approval from the UH Principal Investigator, the ADOC dataset, in whole or in part, will not be further distributed, published, copied, or disseminated in any way or form whatsoever, whether for profit or not. This includes further distributing, copying or disseminating to a different facility or organizational unit within the requesting university, organization, or company.
Modification and Commercial Use: Without prior approval from the University of Houston, the ADOC Dataset, in whole or in part, may not be modified or used for commercial purposes. The license granted herein is specifically for the internal research purposes of Licensee, and Licensee shall not duplicate or use the disclosed ADOC Dataset, its contents, or any seal, logo, mark, or phrase associated with or owned by UH to manufacture, promote, or sell products or technologies (or portions thereof) either directly or indirectly for commercialization or any other direct for-profit purpose without the prior written permission of UH.
Requests for the ADOC Dataset: All requests for the ADOC Dataset will be forwarded to the UH Principal Investigator.
Publication Requirements: Those seeking to include renderings of more than 20 images from the ADOC Dataset in reports, papers, and other documents to be published or released must first obtain approval in writing from the UH Principal Investigator. In no case should the face images be used in a way that could cause the original subject embarrassment or mental anguish.
Citation: All documents and papers that report on research that uses the ADOC Dataset must acknowledge the use of the database by including the citation shown below.
Indemnification: Researcher agrees to indemnify, defend, and hold harmless the University of Houston and its Board of Trustees, officers, employees, and agents, individually and collectively, from any and all losses, expenses, damages, demands, and/or claims based upon any injury or damage (real or alleged) related to, and shall pay all damages, claims, judgments or expenses resulting from, Researcher’s use of the ADOC Dataset.

Contact:
ADOC Principal Investigator:
Email: sshah@central.uh.edu
Mail:
Professor Shishir K Shah,
 Dept. of Computer Science
Philip Guthrie Hoffman Hall
3551 Cullen Blvd., Room 501
Houston, TX 77204-3010

Citation:
@InProceedings{Pranav_2020_ACCV,
    author    = {Pranav, Mantini and Zhenggang, Li and K, Shah Shishir},
    title     = {A Day on Campus - An Anomaly Detection Dataset for Events in a Single Camera},
    booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)},
    month     = {November},
    year      = {2020}
}