TY - GEN
T1 - A new pedestrian dataset for supervised learning
AU - Overett, Gary
AU - Petersson, Lars
AU - Brewer, Nathan
AU - Andersson, Lars
AU - Pettersson, Niklas
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=57749186696&partnerID=8YFLogxK
U2 - 10.1109/IVS.2008.4621297
DO - 10.1109/IVS.2008.4621297
M3 - Conference contribution
SN - 9781424425693
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 373
EP - 378
BT - 2008 IEEE Intelligent Vehicles Symposium, IV
T2 - 2008 IEEE Intelligent Vehicles Symposium, IV
Y2 - 4 June 2008 through 6 June 2008
ER -