Spatiooral Processing for Automatic Vehicle Detection in Wide-Area Aerial Video

Xin Gao, Jeno Szep, Pratik Satam, Salim Hariri, Sundaresh Ram, Jeffrey J. Rodriguez

Research output: Contribution to journalArticlepeer-review

Abstract

Vehicle detection in aerial videos often requires post-processing to eliminate false detections. This paper presents a spatiooral processing scheme to improve automatic vehicle detection performance by replacing the thresholding step of existing detection algorithms with multi-neighborhood hysteresis thresholding for foreground pixel classification. The proposed scheme also performs spatial post-processing, which includes morphological opening and closing to shape and prune the detected objects, and temporal post-processing to further reduce false detections. We evaluate the performance of the proposed spatial processing on two local aerial video datasets and one parking vehicle dataset, and the performance of the proposed spatiooral processing scheme on five local aerial video datasets and one public dataset. Experimental evaluation shows that the proposed schemes improve vehicle detection performance for each of the nine algorithms when evaluated on seven datasets. Overall, the use of the proposed spatiooral processing scheme improves average F-score to above 0.8 and achieves an average reduction of 83.8% in false positives.

Original languageEnglish (US)
Article number9237918
Pages (from-to)199562-199572
Number of pages11
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Keywords

  • Vehicle detection
  • hysteresis thresholding
  • spatiooral processing
  • wide-area aerial imagery

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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