Fully Convolutional Encoder-Decoder With an Attention Mechanism for Practical Pedestrian Trajectory Prediction

Pedestrian trajectory prediction using video is essential for many practical traffic applications. Most existing pedestrian trajectory prediction methods are based on fully connected long short-term memory (LSTM) networks and perform well on public datasets. However, these methods still have three d...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 23; H. 11; S. 20046 - 20060
Hauptverfasser:	Chen, Kai, Song, Xiao, Yuan, Haitao, Ren, Xiaoxiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms attention mechanism Barriers Coders Convolution Convolutional neural networks Data processing Datasets Encoders-Decoders Feature extraction Force Image annotation Image segmentation long short-term memory (LSTM) Markov processes Pedestrian behavior Pedestrians Predictive models Trajectory
ISSN:	1524-9050, 1558-0016
Online-Zugang:	Volltext
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