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

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2016

Author dc:creator

• Bao, Ruxiao

Contributors dc:contributor

• Golparvar Fard, Mani

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

Statement dc:rights

• Copyright 2015 Ruxiao Bao

Language dc:language

en

Handle dc:identifier

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

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/89233