An overview of fast pedestrian detection: feature selection and cascade framework of boosted features
Date
2009
Authors
Zhang, J.
Paisitkriangkrai, S.
Shen, C.
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Conference paper
Citation
Proceedings of the IEEE International Conference on Multimedia and Expo, 2009 (ICME 2009), June 28-July 3, 2009, New York, USA: pp.1566-1567
Statement of Responsibility
Jian Zhang, Sakrapee (Paul) Paisitkriangkrai and Chunhua Shen
Conference Name
IEEE International Conference on Multimedia and Expo 2009, ICME 2009 (2009 : New York, NY)
Abstract
Efficiently and accurately detecting pedestrians plays a crucial role in many vision applications such as video surveillance, multimedia retrieval and smart car etc. 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. Building upon our findings, we propose a new, simpler pedestrian detecting framework based on the covariance features. We conduct feature selection and weak classifier training in the Euclidean space for faster computation. To this end, two machine learning algorithms have been designed: AdaBoost with weighted Fisher linear discriminant analysis (WLDA) based weak classifiers and Greedy Sparse Linear Discriminant Analysis (GSLDA). To further accelerate the detection, we employ a faster strategy, multiple cascade layers with heterogeneous features, to exploit the efficiency of the Haar-like features and the discriminative power of the covariance features. Experimental results shown on different datasets prove that the new pedestrian detection is not only comparable to the performance of the state-of-the-art pedestrian detectors but it also performs at a faster speed.
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© 2009 IEEE