Release Agreement for NTU CCTV-Fights Dataset

The NTU CCTV-Fights Dataset contains 1,000 videos picturing real-world fights, recorded from CCTVs or mobile cameras. We also provide frame-level annotation of each fight instance segment present in the videos, with its exact starting and ending points.

The dataset consists of 280 CCTV videos containing different types of fights, ranging from 5 seconds to 12 minutes, with an average length of 2 minutes. Furthermore, it also contains 720 videos of real fights from other sources (hereinafter referred to as Non-CCTV), mainly from mobile cameras, but a few from car cameras (dash-cams) and drones or helicopters. These videos are shorter, 3 seconds to 7 minutes, with an average length of 45 seconds, but still some have multiple instances of fight and can help the model to generalize better.

The NTU CCTV-Fights Dataset is the property of the Rapid-Rich Object Search (ROSE) Lab at the Nanyang Technological University, Singapore.

Usage for Academic Reseach

Terms and Conditions:

The use of this dataset is governed by the following terms and conditions:

Related Publications

All publications using the NTU CCTV-Fights Dataset should include the following acknowledgement: “(Portions of) the research in this paper used the NTU CCTV-Fights Dataset made available by the ROSE Lab at the Nanyang Technological University, Singapore.” Furthermore, these publications should cite the following reference: . Mauricio Perez, Alex C. Kot, Anderson Rocha, “Detection of Real-world Fights in Surveillance Videos”, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.

By completing this online form, you have accepted the above terms & conditions and agree to include the above acknowledgements in your publications.

We need to validate all requests. Therefore we require you to provide full name of organisation and affiliated email address.

Project Title
Requester's Details
First Name Last Name
Designation example: post-graduate student, research associate, research fellow, faculty, system analyst, data scientist
Email Address
Supervisor's Details (Leave all fields below blank if Supervisor is same as Requester)
First Name Last Name
Email Address
Designation example: research fellow, faculty, lead data scientist, chief data officer