A new pedestrian dataset for supervised learning
- 1 June 2008
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 373-378
- https://doi.org/10.1109/ivs.2008.4621297
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.Keywords
This publication has 12 references indexed in Scilit:
- Human Detection via Classification on Riemannian ManifoldsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Cluster Boosted Tree Classifier for Multi-View, Multi-Pose Object DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Rapid object detection using a boosted cascade of simple featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- WaldBoost — Learning for Time Constrained Sequential DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Example-based object detection in images by componentsIeee Transactions On Pattern Analysis and Machine Intelligence, 2001
- Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)The Annals of Statistics, 2000
- A Trainable System for Object DetectionInternational Journal of Computer Vision, 2000
- Improved Boosting Algorithms Using Confidence-rated PredictionsMachine Learning, 1999