2026-09-23T14:43:43Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/89233 2023-07-11 col_2142_14770 col_2142_5131 com_2142_3518 com_2142_234 com_2142_5130

Golparvar Fard, Mani Bao, Ruxiao 2016-03-02T21:07:03Z 2016-03-02T21:07:03Z 2018-03-03T10:15:31Z 2015-12-08 2015-12 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. Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-12-01 The student, Ruxiao Bao, accepted the attached license on 2015-12-08 at 09:55. The student, Ruxiao Bao, submitted this Thesis for approval on 2015-12-08 at 10:10. This Thesis was approved for publication on 2015-12-08 at 14:59. DSpace SAF Submission Ingestion Package generated from Vireo submission #8962 on 2016-03-02 at 14:13:58 Made available in DSpace on 2016-03-02T21:07:03Z (GMT). No. of bitstreams: 2 BAO-THESIS-2015.pdf: 27446507 bytes, checksum: 5b3e32fcd820ed4799fc466a65988469 (MD5) LICENSE.txt: 4207 bytes, checksum: 4d7402c9454ec7665ea2c5b784b29905 (MD5) Previous issue date: 2015-12-08 Embargo set by: Seth Robbins for item 91436 Lift date: 2018-03-02T21:07:27Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system Limited Restriction Lifted for Item 91436 on 2018-03-03T10:15:31Z. application/pdf http://hdl.handle.net/2142/89233 en Copyright 2015 Ruxiao Bao Kinematic Features Activity Recognition Convolutional Neural Network Construction Equipment Characterizing construction equipment activities in long video sequences of earthmoving operations via kinematic features text text Civil & Environmental Engineering Civil Engineering University of Illinois at Urbana-Champaign Thesis M.S.