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2008

Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features

8 years 5 months ago
Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features
Efficiently and accurately detecting pedestrians plays a very important role in many computer vision applications such as video surveillance and smart cars. In order to find the right feature for this task, we first present a comprehensive experimental study on pedestrian detection using state-of-the-art locally extracted features (e.g., local receptive fields, histogram of oriented gradients, and region covariance). Building upon the findings of our experiments, we propose a new, simpler pedestrian detector using the covariance features. Unlike the work in [1], where the feature selection and weak classifier training are performed on the Riemannian manifold, we select features and train weak classifiers in the Euclidean space for faster computation. To this end, AdaBoost with weighted Fisher linear discriminant analysis-based weak classifiers are designed. A cascaded classifier structure is constructed for efficiency in the detection phase. Experiments on different datasets prove that...
Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhan
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TCSV
Authors Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhang 0002
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