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Description
Title: | Characterizing construction equipment activities in long video sequences of earthmoving operations via kinematic features |
Author(s): | Bao, Ruxiao |
Advisor(s): | Golparvar Fard, Mani |
Department / Program: | Civil & Environmental Engineering |
Discipline: | Civil Engineering |
Degree Granting Institution: | University of Illinois at Urbana-Champaign |
Degree: | M.S. |
Genre: | Thesis |
Subject(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. |
Issue Date: | 2015-12-08 |
Type: | Thesis |
URI: | http://hdl.handle.net/2142/89233 |
Rights Information: | Copyright 2015 Ruxiao Bao |
Date Available in IDEALS: | 2016-03-02 2018-03-03 |
Date Deposited: | 2015-12 |
This item appears in the following Collection(s)
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Dissertations and Theses - Civil and Environmental Engineering
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Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois