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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Fine-Grain Level Sports Video Search Engine

verfasst von : Zikai Song, Junqing Yu, Hengyou Cai, Yangliu Hu, Yi-Ping Phoebe Chen

Erschienen in: MultiMedia Modeling

Verlag: Springer International Publishing

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Abstract

It becomes an urgent demand how to make people find relevant video content of interest from massive sports videos. We have designed and developed a sports video search engine based on distributed architecture, which aims to provide users with content-based video analysis and retrieval services. In sports video search engine, we focus on event detection, highlights analysis and image retrieval. Our work has several advantages: (I) CNN and RNN are used to extract features and integrate dynamic information and a new sliding window model are used for multi-length event detection. (II) For highlights analysis. An improved method based on self-adapting dual threshold and dominant color percentage are used to detect the shot boundary. Affect arousal method are used for highlights extraction. (III) For image’s indexing and retrieval. Hyper-spherical soft assignment method is proposed to generate image descriptor. Enhanced residual vector quantization is presented to construct multi-inverted index. Two adaptive retrieval methods based on hype-spherical filtration are used to improve the time efficient. (IV) All of previous algorithms are implemented in the distributed platform which we develop for massive video data processing.

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Vorheriges Kapitel Efficient HEVC Downscale Transcoding Based on Coding Unit Information Mapping
Nächstes Kapitel The Korean Sign Language Dataset for Action Recognition
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Metadaten
Titel
Fine-Grain Level Sports Video Search Engine
verfasst von
Zikai Song
Junqing Yu
Hengyou Cai
Yangliu Hu
Yi-Ping Phoebe Chen
Copyright-Jahr
2020
Buch
MultiMedia Modeling

Print ISBN: 978-3-030-37730-4

Electronic ISBN: 978-3-030-37731-1

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-37731-1_42

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