Vehicle Detection in UAV Videos Using CNN-SVM

Najiya Koderi Valappil, Qurban A. Memon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Conventional monitoring devices are usually kept at fixed locations which yields a fixed surveillance coverage. Unmanned aerial vehicles (UAVs) are receiving much attention from researchers in traffic monitoring due to their low cost, high flexibility, and wide view. Unlike stationary surveillance, the camera platform of UAVs is in constant motion and makes it difficult to process for data extraction. The inaccuracy in detection rates of vehicles from UAV videos becomes the motivation for combining optical flow methods with supervised learning algorithms. The proposed method incorporates steps that make use of the Kanade–Lucas optical flow method for moving object detection, connected graphs theory and CNN-SVM for further classification. Optical flow generated contains some background objects detected as vehicle when the camera platforms are moving. The classifier rules out the presence of any other moving objects to be detected as vehicles. The proposed method is tested on few stationary and moving aerial videos. The system is found to be 100% accurate in case of stationary aerial videos and 98% accurate in moving videos.

Original languageEnglish
Title of host publicationProceedings of the 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020
EditorsAjith Abraham, Yukio Ohsawa, Niketa Gandhi, M. A. Jabbar, Abdelkrim Haqiq, Seán McLoone, Biju Issac
PublisherSpringer Science and Business Media Deutschland GmbH
Pages221-232
Number of pages12
ISBN (Print)9783030736880
DOIs
Publication statusPublished - 2021
Event12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020 - Virtual, Online
Duration: Dec 15 2020Dec 18 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1383 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020
CityVirtual, Online
Period12/15/2012/18/20

Keywords

  • Aerial video
  • CNN-SVM
  • Kanade-Lucas optical flow
  • Traffic surveillance
  • Traffic video analysis
  • Unmanned aerial vehicle

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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