

Over the last few years, I have been working on the following projects. For code and datasets follow the links below.
 Human Activity Recognition and Tracking
 Developing methods for simultaneous tracking and recognition of
humans performing various activities in a scene. This involves posing
the two problems in a unifying optimization framework that uses the
tracked features to perform better recognition and then uses the
recognized activity to perform better tracking.
 Developing scale and direction invariant Histograms of Oriented Optical Flow (HOOF) features to represent action profiles at a time instant. The nonEuclidean timeseries thus generated from the activity video can be modeled as a NonLinear Dynamical Systems (NLDS).
 Developing an algebraic method for classification of nonEuclidean timeseries data based on the BinetCauchy kernels for NLDS using Mercer kernels defined on the nonEuclidean space.
 CODE:
We provide code for computing Histograms of Oriented Optical Flow (HOOF)  which are features computed at each time instant from a frame of opticalflow vectors to model dynamic phenomena such as human activities. Once a timeseries of HOOF is extracted from a video sequence, these can be modeled as NonLinear Dynamical Systems (NLDS). Kernel PCA is used to learn the parameters of these NLDS and the BinetCauchy kernels for NLDS can be used to compute a distance between pairs of such NLDS.
Dowload (registration required)
 Classification of Linear Dynamical Systems
 Modeling the following visual processes as linear
dynamical systems
 Dynamic Textures
 Human Gaits
 Lip Motion
 Using System ID techniques to recover the models
 Classification, Clustering and Recognition of the
LDS models
 CODE:
The dynamic texture toolbox contains implementation of methods for identifying and comparing video sequences by representing them as Linear Dynamical Systems (LDSs). All code is currently implemented in MATLAB with some code depending on the MATLAB control systems toolbox. A set of tutorials on how to use the toolbox can be found here and the complete documentation can be found here. The code is licensed under the Lesser GPL and is provided for academic purposes. In case of questions, comments, bugs or errors, please contact Avinash Ravichandran (avinash at cis dot jhu dot edu) or Rizwan Chaudhry (rizwanch at cis dot jhu dot edu).
Dowload (registration required)
 Visual Sensor Networks
 Using a network of low power wideangle lens cameras on Intel
Imote2 processors to reconstruct the 3D structure of a scene
 Vision based control of Unmanned Autonomous Vehicles (UAV)
 Developing vision based algorithms to control the
path of a UAV and guiding it through a terrain with potential
minefields.

