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Understanding Human Actions in Video.

Stroud, Jonathan.

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Understanding Human Actions in Video.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding Human Actions in Video./
Author:	Stroud, Jonathan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	144 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-07, Section: B.
Contained By:	Dissertations Abstracts International82-07B.
Subject:	Remote sensing. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28240318
ISBN:	9798684620706

Understanding Human Actions in Video.
Stroud, Jonathan.

Understanding Human Actions in Video. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 144 p.

Source: Dissertations Abstracts International, Volume: 82-07, Section: B.

Thesis (Ph.D.)--University of Michigan, 2020.

This item must not be sold to any third party vendors.

Understanding human behavior is crucial for any autonomous system which interacts with humans. For example, assistive robots need to know when a person is signaling for help, and autonomous vehicles need to know when a person is waiting to cross the street. However, identifying human actions in video is a challenging and unsolved problem. In this work, we address several of the key challenges in human action recognition. To enable better representations of video sequences, we develop novel deep learning architectures which improve representations both at the level of instantaneous motion as well as at the level of long-term context. In addition, to reduce reliance on fixed action vocabularies, we develop a compositional representation of actions which allows novel action descriptions to be represented as a sequence of sub-actions. Finally, we address the issue of data collection for human action understanding by creating a large-scale video dataset, consisting of 70 million videos collected from internet video sharing sites and their matched descriptions. We demonstrate that these contributions improve the generalization performance of human action recognition systems on several benchmark datasets.

ISBN: 9798684620706Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

Computer vision

Understanding Human Actions in Video.
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520 $a Understanding human behavior is crucial for any autonomous system which interacts with humans. For example, assistive robots need to know when a person is signaling for help, and autonomous vehicles need to know when a person is waiting to cross the street. However, identifying human actions in video is a challenging and unsolved problem. In this work, we address several of the key challenges in human action recognition. To enable better representations of video sequences, we develop novel deep learning architectures which improve representations both at the level of instantaneous motion as well as at the level of long-term context. In addition, to reduce reliance on fixed action vocabularies, we develop a compositional representation of actions which allows novel action descriptions to be represented as a sequence of sub-actions. Finally, we address the issue of data collection for human action understanding by creating a large-scale video dataset, consisting of 70 million videos collected from internet video sharing sites and their matched descriptions. We demonstrate that these contributions improve the generalization performance of human action recognition systems on several benchmark datasets.
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