  In this paper, we attempt to advance the research work done in human action
recognition to a rather specialized application namely Indian Classical Dance
(ICD) classification. The variation in such dance forms in terms of hand and
body postures, facial expressions or emotions and head orientation makes pose
estimation an extremely challenging task. To circumvent this problem, we
construct a pose-oblivious shape signature which is fed to a sequence learning
framework. The pose signature representation is done in two-fold process.
First, we represent person-pose in first frame of a dance video using symmetric
Spatial Transformer Networks (STN) to extract good person object proposals and
CNN-based parallel single person pose estimator (SPPE). Next, the pose basis
are converted to pose flows by assigning a similarity score between successive
poses followed by non-maximal suppression. Instead of feeding a simple chain of
joints in the sequence learner which generally hinders the network performance
we constitute a feature vector of the normalized distance vectors, flow, angles
between anchor joints which captures the adjacency configuration in the
skeletal pattern. Thus, the kinematic relationship amongst the body joints
across the frames using pose estimation helps in better establishing the
spatio-temporal dependencies. We present an exhaustive empirical evaluation of
state-of-the-art deep network based methods for dance classification on ICD
dataset.
