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oai:www.ideals.illinois.edu:2142/46892 2023-07-11 col_2142_5131 col_2142_14770 com_2142_5130 com_2142_3518 com_2142_234

Golparvar-Fard, Mani Khosrowpour, Ardalan 2014-01-16T18:25:29Z 2014-01-16T18:25:29Z 2016-01-16T11:01:53Z 2013-12 2014-01-16T18:25:29Z 2013-12 Workface assessment –the process of determining the overall activity rates of onsite construction workers throughout a day– typically involves manual visual observations which are time-consuming and labor-intensive. To minimize subjectivity and the time required for conducting detailed assessments, and allowing managers to spend their time on the more important task of assessing and implementing improvements, we propose a new inexpensive vision-based method using RGB-D sensors that is applicable to interior construction operations. This is particularly a challenging task as construction activities have a large range of intra-class variability including varying sequences of body posture and time-spent on each individual activity. On the other hand, the state-of-the-art skeleton extraction algorithms from RGB-D sequences are not robust enough especially when workers interact with tools or self-occlude the camera’s field-of-view. Existing vision-based methods are also rather limited as they can primarily classify “atomic” activities from RGB-D sequences involving one worker conducting a single activity. To address these limitations, our proposed original method involves three main components: 1) an algorithm for detecting, tracking, and extracting body skeleton features from depth images; 2) A discriminative bag-of-poses activity classifier trained using multiple Support Vector Machines for classifying single visual activities from a given body skeleton sequence; and 3) a Hidden Markov model with a Kernel Density Estimation function to represent emission probabilities in form of a statistical distribution of single activity classifiers. For training and testing purposes, we also introduce a new dataset of eleven RGB-D sequences for interior drywall construction operations involving three actual construction workers conducting eight different activities in various interior locations. Our experimental results with an average accuracy of 76% on the testing dataset show the promise of vision-based methods using RGB-D sequences for facilitating the activity analysis workface assessment. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-12-12T19:39:08Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 3 Khosrowpour_Ardalan.pdf: 1734451 bytes, checksum: 4ac6df276cc7f4a8750c35c1a508bba1 (MD5) Khosrowpour_Ardalan.docx: 11320392 bytes, checksum: 7f5d424d608c67b277bafac84c9c9c39 (MD5) Khosrowpour_Ardalan.pdf: 1740594 bytes, checksum: 1cffda78fee2c0f12e1c79f05b3ece0c (MD5) Made available in DSpace on 2014-01-16T18:25:29Z (GMT). No. of bitstreams: 4 Ardalan_Khosrowpour.pdf: 1725370 bytes, checksum: ad236d0b24bd8c2a3255de49c5b12740 (MD5) Khosrowpour_Ardalan.docx: 11320783 bytes, checksum: c352e7859b4f55c48a18cf01f2cad13d (MD5) 1_Khosrowpour_Ardalan.docx: 11320392 bytes, checksum: 7f5d424d608c67b277bafac84c9c9c39 (MD5) license.txt: 4069 bytes, checksum: 9886df77281d564570b1585b432b21f4 (MD5) Restriction data tranferred 2014-07-01T11:36:44-05:00 Original Data Group with Access Administrator Release Date: 2016-01-16 12:27:27 UTC Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system Item marked as restricted to the 'Administrator' Group (id=1) by Seth Robbins (robbins.sd@gmail.com) on 2014-01-16T18:27:31Z Item is restricted until 2016-01-16T18:27:27Z Limited Restriction Lifted for Item 46911 on 2016-01-16T11:01:53Z. http://hdl.handle.net/2142/46892 en Copyright © 2013 Ardalan Khosrowpour Activity Analysis Workface Assessment RGB-D (RedGreenBlue-Depth) cameras Hidden Markov ModelActivity analysis Hidden Markov Model Vision-based workface assessment using depth images for activity analysis of interior construction operations text Civil & Environmental Eng 1251 Civil Engineering 0106 University of Illinois at Urbana-Champaign Thesis M.S. MS:Civil Engineering -UIUC 10KS0106MS