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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.