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Erschienen in:
Constrained Spectral Clustering on Face Annotation System Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Gait Retrieval: A Deep Hashing Method for People Retrieval in Video

verfasst von : Muhammad Rauf, Yongzhen Huang, Liang Wang

Erschienen in: Pattern Recognition

Verlag: Springer Singapore

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Abstract

Automated surveillance systems are required for the state of the art security. Everyday networks of cameras generate a very-large set of data, which makes recognition and identification tasks harder. In this paper, we present a new problem called Gait Retrieval in order to address the challenge of large-scale surveillance data. We have an interest in retrieving similar videos based on the human gait. Gait is the most important biometric for long distance human identification. We also propose a solution for the Gait Retrieval problem by using gait biometrics. The solution is based on the deep hashing technique to learn a hash function that preserves the similarities between the same labeled images. Deep hash function with convolutional neural network learns features and maps them to hash codes. Images with similar appearance should have similar hash codes. Training samples are arranged in a batch of triplets. Our proposed method outperforms traditional methods with good margin.

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Vorheriges Kapitel Novel Face Hallucination Through Patch Position Based Multiple Regressors Fusion
Nächstes Kapitel Background Subtraction Based on Superpixels Under Multi-scale in Complex Scenes
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Metadaten
Titel
Gait Retrieval: A Deep Hashing Method for People Retrieval in Video
verfasst von
Muhammad Rauf
Yongzhen Huang
Liang Wang
Copyright-Jahr
2016
Verlag
Springer Singapore
Buch
Pattern Recognition

Print ISBN: 978-981-10-3001-7

Electronic ISBN: 978-981-10-3002-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-981-10-3002-4_32

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