Adaptive Deformation Handling for Pedestrian Detection

Despite the abundance of successful models for pedestrian detection, many are limited in their ability to handle deformations, such as large appearance variations. In view of insufficient number of models with the ability to handle deformations, we propose a simple strategy, which incorporates defor...

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Bibliographic Details
Main Authors: Hak Kyoung Kim, Yonghyun Kim, Daijin Kim
Format: Conference Proceeding
Language:English
Subjects:
Boosting
Deformable models
Detectors
Feature extraction
Object recognition
Training
Vegetation
Online Access:Request full text
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Summary:Despite the abundance of successful models for pedestrian detection, many are limited in their ability to handle deformations, such as large appearance variations. In view of insufficient number of models with the ability to handle deformations, we propose a simple strategy, which incorporates deformation handling with a spatial pyramid method in basic classifier learning. By using the max pooling method, this approach aggregates a set of randomly selected basic features from a local region. The spatial pyramid method has been integrated to our method to construct a richer feature in a local region. We show how to train the model with this deformation handling method using a boosting process. Our best detector outperforms the state-of-the-art of pedestrian detection on the INRIA and the Caltech-USA datasets. It achieves a log average miss rate of 12.21% on the INRIA and a log average miss rate of 24.03% on the Caltech-USA datasets.
ISSN:1550-5790
2642-9381
DOI:10.1109/WACV.2015.28