Change Detection in Stochastic Shape Dynamical Models with Applications in Activity Modeling and Abnormality Detection  

    The aim is to model "activity" performed by a group of moving and interacting objects (which can be people or cars or different rigid components of the human body) and use the models for tracking, abnormal activity detection and segmentation. We treat the objects as point objects (referred to as `landmarks') and model their changing configuration as a moving and deforming "shape" using ideas from Kendall's shape theory for discrete landmarks. A continuous state HMM which takes the objects' configuration as the observation and the shape and motion as the hidden state, is defined to represent an activity and called a "shape activity". Particle filters are used to track the :HMM.  More
   
An abnormal activity is defined as a change in the shape activity model, which could be slow or drastic and whose parameters are unknown. Drastic changes can be detected easily using the increase in tracking error or the negative log of observation likelihood. But slow changes usually get missed. We propose a statistic for slow change detection & study the modeling & particle filtering errors in its approximation.  More
   
 Besides this, I have also worked on image classification. I proposed a linear subspace algorithm for pattern (image/video) classification which was motivated by PCA. It attempts to approximate the optimal Bayes classifier for Gaussian class conditional distributions with unequal covariance matrices. I also analyzed its classification error performance, compared it with LDA and showed experimental results on various image classification and video retrieval problems.  More
   
During my first year in graduate school, I worked on fast algorithms for infra-red image compression. More