Title: A multi-camera video dataset for research on high-definition surveillance

Authors: Athira Nambiar; Matteo Taiana; Dario Figueira; Jacinto C. Nascimento; Alexandre Bernardino

Addresses: Computer and Robot Vision Lab, Institute for Systems and Robotics, Instituto Superior Técnico, VisLab – Torre Norte, 7th floor, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal ' Computer and Robot Vision Lab, Institute for Systems and Robotics, Instituto Superior Técnico, VisLab – Torre Norte, 7th floor, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal ' Computer and Robot Vision Lab, Institute for Systems and Robotics, Instituto Superior Técnico, VisLab – Torre Norte, 7th floor, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal ' Computer and Robot Vision Lab, Institute for Systems and Robotics, Instituto Superior Técnico, VisLab – Torre Norte, 7th floor, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal ' Computer and Robot Vision Lab, Institute for Systems and Robotics, Instituto Superior Técnico, VisLab – Torre Norte, 7th floor, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal

Abstract: We present a fully labelled image sequence dataset for benchmarking video surveillance algorithms. The dataset was acquired from 13 indoor cameras distributed over three floors of one building, recording simultaneously for 30 minutes. The dataset was specially designed and labelled to tackle the person detection and re-identification problems. Around 80 persons participated in the data collection, most of them appearing in more than one camera. The dataset is heterogeneous: there are three distinct types of cameras (standard, high and very high resolution), different view types (corridors, doors, open spaces) and different frame rates. This diversity is essential for a proper assessment of the robustness of video analytics algorithms in different imaging conditions. We illustrate the application of pedestrian detection and re-identification algorithms to the given dataset, pointing out important criteria for benchmarking and the impact of high-resolution imagery on the performance of the algorithms.

Keywords: video surveillance; pedestrian detection; re-identification; benchmarking; image sequence datasets; camera networks; high definition; signal processing; image sequencing; high resolution; camera resolution; indoor cameras; multiple cameras.

DOI: 10.1504/IJMISSP.2014.066428

International Journal of Machine Intelligence and Sensory Signal Processing, 2014 Vol.1 No.3, pp.267 - 286

Received: 22 Jul 2013
Accepted: 24 Dec 2013
Published online: 31 Dec 2014 *

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