Convolutional neural network-based shadow detection in images using visible light camera sensor

Dong Seop Kim, Muhammad Arsalan, Kang Ryoung Park

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works.

Original languageEnglish
Article number960
JournalSensors
Volume18
Issue number4
DOIs
StatePublished - Apr 2018

Keywords

  • CNN
  • Color feature
  • Intelligence surveillance camera
  • Shadow detection

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