Vision-Based Excavator Detection and Tracking Using Hybrid Kinematic Shapes and Key Nodes-Reference-Cited by-同舟云学术

Vision-Based Excavator Detection and Tracking Using Hybrid Kinematic Shapes and Key Nodes

Published:2017-01 Issue:1 Volume:31 Page:04016038
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Yuan Chenxi¹,Li Shuai¹,Cai Hubo²

Affiliation:

1. Ph.D. Student, Division of Construction Engineering and Management, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907.

2. Associate Professor, Division of Construction Engineering and Management, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000602

Reference42 articles.

1. 24bosoxfan. (2011). “Cat 336D excavator.” ⟨https://www.youtube.com/watch?v=5l0skWIUmcM⟩ (Oct. 13 2011).

2. Automated Visual Recognition of Dump Trucks in Construction Videos

3. Part based model and spatial–temporal reasoning to recognize hydraulic excavators in construction images and videos

4. Azar E. R. and McCabe B. (2012c). “Vision-based recognition of dirt loading cycles in construction sites.” Construction Research Congress 2012 ASCE Reston VA 1042–1051.

5. Bernardo J. M. and Smith A. F. (2009). Bayesian theory Wiley Hoboken NJ.

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