University of Illinois Urbana-Champaign

Fully automatic vision-based system for vehicle crash prediction and recognition

Khorrami, Pooya

Content Files
Pooya_Khorrami.pdf

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https://hdl.handle.net/2142/46867

Description

Title
Fully automatic vision-based system for vehicle crash prediction and recognition
Author(s)
Khorrami, Pooya
Issue Date
2014-01-16T18:19:08Z
Director of Research (if dissertation) or Advisor (if thesis)
Huang, Thomas S.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2014-01-16T18:19:08Z
Keyword(s)
  • Vision-Based Surveillance System
  • Robust Principal Component Analysis (PCA)
  • Kalman Filter
  • Accident Prediction
  • Accident Recognition
  • Background Subtraction
  • Object Tracking
Abstract
Just as they were half a century ago, automobile accidents are, unfortunately, one of the leading causes of death today. Therefore, it is no surprise that automated traffic analysis systems generate a supreme amount of interest. Despite the rapid advances in technology today, many traffic monitoring systems require substantial amounts of careful annotation. As such, a fully automated traffic analysis system that can perform accident prediction would be highly beneficial to multiple parties. Such systems would make a traffic analyst's workload more manageable and would provide a more sophisticated tool for determining the root causes of traffic accidents. In this thesis, we present an automatic vision-based system for both accident prediction and recognition. Our method first detects and tracks vehicles using Robust Principal Component Analysis (Robust PCA) and Kalman Filters in order to extract trajectories. Pairs of vehicles trajectories are then segmented and classified by a Support Vector Machine (SVM) in order to determine the likelihood of a collision. We also tackle the problem of accident recognition by classifying crashing trajectory pairs into distinct categories. An ontology is used to define the relationships between the accident types and to train a tree-based classifier for recognition. We demonstrate the effectiveness of each algorithm by evaluating them on a crash dataset provided by the Toyota Motor Corp.
Graduation Semester
2013-12
Permalink
http://hdl.handle.net/2142/46867
Copyright and License Information
Copyright 2013 Pooya Khorrami

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