A corpus for benchmarking of people detection algorithms

This paper describes a corpus, dataset and associated ground-truth, for the evaluation of people detection algorithms in surveillance video scenarios, along with the design procedure followed to generate it. Sequences from scenes with different levels of complexity have been manually annotated. Each person present at a scene has been labeled frame by frame, in order to automatically obtain a people detection ground-truth for each sequence. Sequences have been classified into different complexity categories depending on critical factors that typically affect the behavior of detection algorithms. The resulting corpus, which exceeds other public pedestrian datasets in the amount of video sequences and its complexity variability, is freely available for benchmarking and research purposes under a license agreement.

► We present a more complete people detection corpus in surveillance scenarios.
► We present a comparison with other public people detection datasets.
► We realize an exhaustive complexity analysis and video classification.
► We provide a common framework for the evaluation of people detection algorithms under different complexity conditions.