Configuration Estimates Improve Pedestrian Finding

Tran, Duan; Forsyth, D.A.

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https://hdl.handle.net/2142/11330 Copy

Description

Title

Configuration Estimates Improve Pedestrian Finding

Author(s)

• Tran, Duan
• Forsyth, D.A.

Issue Date

2007-04

Keyword(s)

computer science

Abstract

Fair discriminative pedestrian finders are now available. However, these pedestrian finders make most errors on pedestrians in configurations that are uncommon in the training data, for example, mounting a bicycle. This is undesirable. However, the human configuration can itself be estimated discriminatively using structure learning. We demonstrate a pedestrian finder which first finds the most likely human pose in the window using a discriminative procedure trained with structure learning on a small dataset. We then present features (local histogram of oriented gradient and local PCA of gradient) based on that configuration to an SVM classifier. We show, using the INRIA Person dataset, that estimates of configuration significantly improve the accuracy of a discriminative pedestrian finder but require fewer training examples.

Type of Resource

text

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http://hdl.handle.net/2142/11330

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