Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158618
Title: Learning to anticipate and forecast human actions from videos
Authors: Peh, Eric Zheng Quan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Peh, E. Z. Q. (2022). Learning to anticipate and forecast human actions from videos. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158618
Abstract: Action Anticipation and forecasting aims to predict future actions by processing videos containing past and current observations. In this project, we develop new methods based on the encoder-decoder architecture with Transformer models to anticipate and forecast future human actions by processing videos. The model will observe a video for several seconds (or minutes) and then encodes information of the video to predict plausible human action that are going to happen in the future. Temporal information from videos will be extracted from deep neural networks. The performance of these models will then be evaluated on standard action forecasting datasets such as Breakfast and 50Salads datasets
URI: https://hdl.handle.net/10356/158618
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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