Human Activity Recognition

Most video surveillance systems can only provide simple event alarms such as Virtual Perimeter Breach, in which a person crosses an imaginary line (Left), or Abandoned Object Detection.  Our goal is to detect more complex multi-persons’ actions such as two people interacting (e.g., shaking hands) or a person interacting with an object (e.g., gun-shooting). This is useful for online surveillance or video database indexing.

Related Publications:

  1. S. Althloothi, M.H. Mahoor, X. Zhang, R. M. Voyles, “Human Activity Recognition Using Multi-Features and Multiple Kernel Learning”, Journal of Pattern Recognition, Volume 47, Issue 5, Pages 1800–1812, May 2014.
  2. Wu, G., Mahoor, M.H., Althloothi, S., Voyles, R., “SIFT-Motion Estimation (SIFT-ME): A New Feature for Human Activity Recognition”, The 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition, Los Vegas, July 2010.
  3. Althloothi, S., Voyles, R., Mahoor, M.H., Wu, G., “2D Human Skeleton Model from Monocular Video for Human Activity Recognition”, The 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition, Los Vegas, July 2010.
  4. Salah Althloothi, Mohammad H. Mahoor, Richard M. Voyles, “A Robust Method for Rotation Estimation Using Spherical Harmonics Representation”, IEEE Transactions on Image Processing. pp. 2306-2316, Vol. 22, No.6 June 2013.
  5. S. Althloothi, M. H. Mahoor, and R. M. Voyles, “Fitting distal limb segments for accurate skeletonization in human action recognition”, Journal of Ambient Intelligence and Smart Environments, Volume 4 (2) IOS Press – Jan 1, 2012.