Multitask Learning Method for Detecting the Visual Focus of Attention of Construction Workers-Reference-Cited by-同舟云学术

Multitask Learning Method for Detecting the Visual Focus of Attention of Construction Workers

Published:2021-07 Issue:7 Volume:147 Page:
ISSN:0733-9364
Container-title:Journal of Construction Engineering and Management
language:en
Short-container-title:J. Constr. Eng. Manage.

Author:

Cai Jiannan¹^ORCID,Yang Liu²,Zhang Yuxi²,Li Shuai³,Cai Hubo⁴

Affiliation:

1. Assistant Professor, Dept. of Construction Science, Univ. of Texas at San Antonio, 501 W César E Chávez Blvd., San Antonio, TX 78207 (corresponding author). ORCID: .

2. Ph.D. Student, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907.

3. Assistant Professor, Dept. of Civil and Environmental Engineering, Univ. of Tennessee, 851 Neyland Dr., Knoxville, TN 37996.

4. Professor, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Strategy and Management,Industrial relations,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CO.1943-7862.0002071

Reference48 articles.

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2. Asteriadis S. K. Karpouzis and S. Kollias. 2011. “Robust validation of visual focus of attention using adaptive fusion of head and eye gaze patterns.” In Proc. IEEE Int. Conf. on Computer Vision 414–421. New York: IEEE.

3. An Adaptive Motion Model for Person Tracking with Instantaneous Head-Pose Features

4. Cai J. L. Yang Y. Zhang and H. Cai. 2020a. “Estimating the visual attention of construction workers from head pose using convolutional neural network-based multi-task learning.” In Proc. Construction Research Congress 2020: Computer Applications 116–124. Denver: Construction Institute of ASCE.

5. Two-step long short-term memory method for identifying construction activities through positional and attentional cues

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