Shadow Removal for Action Recognition in a Smart Condo Environment

• Author / Creator

  Eskandari, Samaneh

• Depending on the method we choose for human action recognition, some algorithms require the localization of human in action videos as a preprocessing step. This preprocessing is more challenging in real environments with noises or varying illumination, and may include background subtraction, shadow removal and noise removal.

  This thesis concerns with shadow removal problem with the goal of extracting human silhouette properly. Detecting shadows can be considered as a labeling problem and can be transformed into an energy minimization problem in a Markov Random Field framework. The advantage of the algorithm is that it considers the neighborhood information as well as chromaticity values of the pixels.

  Also a new dataset is provided containing several daily actions of people living in a smart condo. The dataset is used in order to evaluate the proposed method. Moreover, it can be useful in action recognition and motion analysis studies with health care applications.

• Subjects / Keywords

  • Smart Condo
  • Shadow Removal
  • Shadow Detection
  • Human Action Dataset

• Graduation date

  Spring 2014

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3KH4T

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.