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Erschienen in:
Deep Graph Laplacian Hashing for Image Retrieval Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Temporal Interval Regression Network for Video Action Detection

verfasst von : Qing Wang, Laiyun Qing, Jun Miao, Lijuan Duan

Erschienen in: Advances in Multimedia Information Processing – PCM 2017

Verlag: Springer International Publishing

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Abstract

Temporal action detection in untrimmed video is an important and challenging task in computer vision. In this paper, a straightforward and efficient regression model is proposed by us to detect action instance and refine action interval in long untrimmed videos. We train a single 3D Convolutional Networks (3D ConvNets) jointly with two sibling output layers: a classification layer to predict the class label and a temporal interval regression layer to modify the temporal localization of input proposal. We also introduce an effective method to sample negative and positive proposals which are discriminative to feature extractor and classifier during training. On THUMOS 2014 dataset, our method achieves competitive performance compared with recent state-of-the-art methods.

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Vorheriges Kapitel Trajectory-Pooled 3D Convolutional Descriptors for Action Recognition
Nächstes Kapitel Semantic Sequence Analysis for Human Activity Prediction
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Metadaten
Titel
Temporal Interval Regression Network for Video Action Detection
verfasst von
Qing Wang
Laiyun Qing
Jun Miao
Lijuan Duan
Copyright-Jahr
2018
Buch
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77379-7

Electronic ISBN: 978-3-319-77380-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77380-3_25

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