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Histogram of confidences for person detection

Histogram of confidences for person detection
Histogram of confidences for person detection
This paper focuses on the problem of person detection in harsh industrial environments. Different image regions often have different requirements for the person to be detected. Additionally, as the environment can change on a frame to frame basis even previously detected people can fail to be found. In our work we adapt a previously trained classifier to improve its performance in the industrial environment. The classifier output is initially used an image descriptor. Structure from the descriptor history is learned using semi-supervised learning to boost overall performance. In comparison with two state of the art person detectors we see gains of 10%. Our approach is generally applicable to pretrained classifiers which can then be specialised for a specific scene
Image analysis, Image classification, Object detection, Identification of persons, Image segmentation
978-1-4244-7993-1
1841-1844
Middleton, Lee
f165a2fa-1a66-4d84-9c58-0cdaa8e73272
Snowdon, James R.
48a26581-eba4-41ed-955d-ca84e5aa6908
Middleton, Lee
f165a2fa-1a66-4d84-9c58-0cdaa8e73272
Snowdon, James R.
48a26581-eba4-41ed-955d-ca84e5aa6908

Middleton, Lee and Snowdon, James R. (2010) Histogram of confidences for person detection. 17th IEEE International Conference on Image Processing, Hong Kong. 26 - 29 Sep 2010. pp. 1841-1844 .

Record type: Conference or Workshop Item (Other)

Abstract

This paper focuses on the problem of person detection in harsh industrial environments. Different image regions often have different requirements for the person to be detected. Additionally, as the environment can change on a frame to frame basis even previously detected people can fail to be found. In our work we adapt a previously trained classifier to improve its performance in the industrial environment. The classifier output is initially used an image descriptor. Structure from the descriptor history is learned using semi-supervised learning to boost overall performance. In comparison with two state of the art person detectors we see gains of 10%. Our approach is generally applicable to pretrained classifiers which can then be specialised for a specific scene

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More information

Submitted date: September 2010
Published date: September 2010
Additional Information: Event Dates: September 26-29, 2010
Venue - Dates: 17th IEEE International Conference on Image Processing, Hong Kong, 2010-09-26 - 2010-09-29
Keywords: Image analysis, Image classification, Object detection, Identification of persons, Image segmentation
Organisations: Electronics & Computer Science, IT Innovation

Identifiers

Local EPrints ID: 272040
URI: http://eprints.soton.ac.uk/id/eprint/272040
ISBN: 978-1-4244-7993-1
PURE UUID: 5becdc0e-0cd3-416c-a05d-893bd697caa5

Catalogue record

Date deposited: 17 Feb 2011 11:32
Last modified: 14 Mar 2024 09:45

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Contributors

Author: Lee Middleton
Author: James R. Snowdon

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