Next Article in Journal
CS2-Collector: A New Approach for Data Collection in Wireless Sensor Networks Based on Two-Dimensional Compressive Sensing
Next Article in Special Issue
Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model
Previous Article in Journal
Modeling and Analysis of Micro-Spacecraft Attitude Sensing with Gyrowheel
Previous Article in Special Issue
An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(8), 1325; doi:10.3390/s16081325

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

1
Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
2
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Academic Editors: Felipe Gonzalez Toro and Antonios Tsourdos
Received: 25 June 2016 / Revised: 5 August 2016 / Accepted: 15 August 2016 / Published: 19 August 2016
(This article belongs to the Special Issue UAV-Based Remote Sensing)

Abstract

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians. View Full-Text
Keywords: vehicle detection; unmanned aerial vehicle; Viola-Jones; HOG; road orientation vehicle detection; unmanned aerial vehicle; Viola-Jones; HOG; road orientation
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary materials

  • Supplementary File 1:

    Supplementary (ZIP, 10863 KB)

  • Externally hosted supplementary file 1
    Doi: null
    Link: http://null
    Description: GIF files

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Xu, Y.; Yu, G.; Wang, Y.; Wu, X.; Ma, Y. A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images. Sensors 2016, 16, 1325.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top