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Abstract

We study the question of activity classification in videos and present a novel approach for recognizing
human action categories in videos by combining information from appearance and motion of human body parts.
Our approach uses a tracking step which involves Particle Filtering and a local non - parametric clustering step.
The motion information is provided by the trajectory of the cluster modes of a local set of particles. The statistical
information about the particles of that cluster over a number of frames provides the appearance information. Later
we use a “Bag ofWords” model to build one histogram per video sequence from the set of these robust appearance
and motion descriptors. These histograms provide us characteristic information which helps us to discriminate
among various human actions and thus classify them correctly.
We tested our approach on the standard KTH and Weizmann human action datasets and the results were comparable
to the state of the art. Additionally our approach is able to distinguish between activities that involve the
motion of complete body from those in which only certain body parts move. In other words, our method discriminates
well between activities with “gross motion” like running, jogging etc. and “local motion” like waving,
boxing etc.