Fusion of Multi-Intensity Image for Deep Learning-Based Human and Face Detection

Peggy Joy Lu*, Jen Hui Chuang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

For ordinary IR-illuminators in nighttime surveillance system, insufficient illumination may cause misdetection for faraway object while excessive illumination leads to over-exposure of nearby object. To overcome these two problems, we use the MI3 image dataset, which is established by multi-intensity IR-illumination (MIIR), as our benchmark dataset for modern object detection methods. We first provide complete annotations for the MI3 as its current ground-truth is incomplete. Then, we use these multi-intensity illuminated IR videos to evaluate several widely used object detectors, i.e., SSD, YOLO, Faster R-CNN, and Mask R-CNN, by analyzing the effective range of different illumination intensities. By including a tracking scheme, as well as developing of a new fusion method for different illumination intensities to improve the performance, the proposed approach may serve as a new benchmark of face and object detection for a wide range of distances. The new dataset (Dataset is available: https://ieee-dataport.org/documents/mi3) with more complete annotations and source codes (Codes are available: https://github.com/thesuperorange/deepMI3) is available online.

Original languageEnglish
Pages (from-to)8816-8823
Number of pages8
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Keywords

  • Detectors
  • Face detection
  • Face recognition
  • Lighting
  • Object detection
  • Surveillance
  • Videos

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