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Part Based Recognition of Pedestrians Using Multiple Features and Random Forests

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Open access
Authors
John, Gladis
West, Geoffrey
Lazarescu, Mihai
Date
2010
Type
Conference Paper

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John, Gladis S. and West, Geoffrey A.W. and Lazarescu, Mihai. 2010. Part Based Recognition of Pedestrians Using Multiple Features and Random Forests, in Zhang, J. and Shen, C. and Geers, G. and Wu, Q. (ed), 2010 International Conference on Digital Image Computing: Techniques and Applications, Dec 3 2010. Sydney, NSW: IEEE.
Source Title
Proceedings of 2010 International Conference on Digital Image Computing: Techniques and Applications
Source Conference
2010 International Conference on Digital Image Computing: Techniques and Applications
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Department of Computing
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Abstract

This paper explores a discriminative part-based approach for recognising people in video. It uses many regions to model the background and foreground and a random forest for classification. The objective is to overcome the limitations of more holistic approaches that try to recognise people as a single region with the consequential need to segment each person as one representation. Attributes of each blob, their relationships and variation over video frames are argued to be useful features for discrimination. In this paper the attributes of each blob are considered as a first step in the recognition process. We evaluate our approach through a comparison of three state of the art classifiers: Bagging, Adaboost and a Multilayer Perceptron (MLP), with the Random Forest (RF) using 10 fold cross validation. A detailed statistical analysis shows that the random forest classifier is more accurate compared to the other methods in terms of discrimination between regions describing people and those of the background.