A new pedestrian dataset for supervised learning

Gary Overett*, Lars Petersson, Nathan Brewer, Lars Andersson, Niklas Pettersson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

113 Citations (Scopus)

Abstract

This paper presents a comparative analysis of different pedestrian dataset characteristics. The main goal of the research is to determine what characteristics are desirable for improved training and validation of pedestrian detectors and classifiers. The work focuses on those aspects of the dataset which affect classification success using the most common boosting methods. Dataset characteristics such as image size, aspect ratio, geometric variance and the relative scale of positive class instances (pedestrians) within the training window form an integral part of classification success. This paper will examine the effects of varying these dataset characteristics with a view to determining the recommended attributes of a high quality and challenging dataset. While the primary focus is on characteristics of the positive training dataset, some discussion of desirable attributes for the negative dataset is important and is therefore included. This paper also serves to publish our current pedestrian dataset in various forms for non-commercial use by the scientific community. We believe the published dataset to be one of the largest, most flexible, and representative datasets available for pedestrian/person detection tasks.

Original languageEnglish
Title of host publication2008 IEEE Intelligent Vehicles Symposium, IV
Pages373-378
Number of pages6
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 IEEE Intelligent Vehicles Symposium, IV - Eindhoven, Netherlands
Duration: 4 Jun 20086 Jun 2008

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference2008 IEEE Intelligent Vehicles Symposium, IV
Country/TerritoryNetherlands
CityEindhoven
Period4/06/086/06/08

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