Characterizing construction equipment activities in long video sequences of earthmoving operations via kinematic features

Bao, Ruxiao

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https://hdl.handle.net/2142/89233 Copy

Description

Title

Characterizing construction equipment activities in long video sequences of earthmoving operations via kinematic features

Author(s)

Bao, Ruxiao

Issue Date

2015-12-08

Director of Research (if dissertation) or Advisor (if thesis)

Golparvar Fard, Mani

Department of Study

Civil & Environmental Engineering

Discipline

Civil Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Kinematic Features
• Activity Recognition
• Convolutional Neural Network
• Construction Equipment

Abstract

This thesis presents a fast and scalable method for activity analysis of construction equipment involved in earthmoving operations from highly varying long-sequence videos obtained from fixed cameras. A common approach to characterize equipment activities consists of detecting and tracking the equipment within the video volume, recognizing interest points and describing them locally, followed by a bag-of-words representation for classifying activities. While successful results have been achieved in each aspect of detection, tracking, and activity recognition, the highly varying degree of intra-class variability in resources, occlusions and scene clutter, the difficulties in defining visually-distinct activities, together with long computational time have challenged scalability of current solutions. In this thesis, we present a new end-to-end automated method to recognize the equipment activities by simultaneously detecting and tracking features, and characterizing the spatial kinematics of features via a decision tree. The method is tested on an unprecedented dataset of 5hr-long real-world videos of interacting pairs of excavators and trucks. The Experimental results show that the method is capable of activity recognition with accuracy of 88.91% with a computational time less than 1- to-1 ratio for each video length. The benefits of the proposed method for root-cause assessment of performance deviations are discussed.

Graduation Semester

2015-12

Type of Resource

text

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http://hdl.handle.net/2142/89233

Copyright and License Information

Copyright 2015 Ruxiao Bao

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