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Automated Activity Recognition of Construction Equipment Using a Data Fusion Approach

EasyChair Preprint no. 1240

8 pagesDate: June 27, 2019


Automated monitoring of construction operations, especially operations of equipment and machines, is an essential step toward cost-estimating, and planning of construction projects. In recent years, a number of methods were suggested for recognizing activities of construction equipment. These methods are based on processing single types of data (audio, visual, or kinematic data). Considering the complexity of construction jobsites, using one source of data is not reliable enough to cover all conditions and scenarios. To address the issue, we utilized a data fusion approach: This approach is based on collecting audio and kinematic data, and includes the following steps: 1) recording audio and kinematic data generated by machines, 2) preprocessing data, 3) extracting time- and frequency-domain-features, 4) feature-fusion, and 5) categorizing activities using a machine-learning algorithm. The proposed approach was implemented on multiple machines and the experiments show that it is possible to get up to 25% more-accurate results compared to cases of using single-data-sources.

Keyphrases: activity recognition, Audio and Kinematic Data, Construction Equipment, feature fusion, machine learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Behnam Sherafat and Abbas Rashidi and Yong-Cheol Lee and Changbum R. Ahn},
  title = {Automated Activity Recognition of Construction Equipment Using a Data Fusion Approach},
  howpublished = {EasyChair Preprint no. 1240},

  year = {EasyChair, 2019}}
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