Predicting 360° Video Saliency: A ConvLSTM Encoder-Decoder Network With Spatio-Temporal Consistency

360° videos have been widely used with the development of virtual reality technology and triggered a demand to determine the most visually attractive objects in them, aka 360° video saliency prediction (VSP). While generative models, i.e., variational autoencoders or autoregressive models have prove...

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Veröffentlicht in:	IEEE journal on emerging and selected topics in circuits and systems Jg. 14; H. 2; S. 311 - 322
Hauptverfasser:	Wan, Zhaolin, Qin, Han, Xiong, Ruiqin, Li, Zhiyang, Fan, Xiaopeng, Zhao, Debin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.06.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	360° videos Active appearance model Autoregressive models Circuits and systems Computer architecture Convolutional neural networks Distortion Effectiveness Encoders-Decoders Feature extraction Gaussian priors Modules Salience Saliency prediction Solid modeling spatio-temporal features Spatiotemporal data Video Virtual reality
ISSN:	2156-3357, 2156-3365
Online-Zugang:	Volltext
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