  Manifold amount of video data gets generated every minute as we read this
document, ranging from surveillance to broadcasting purposes. There are two
roadblocks that restrain us from using this data as such, first being the
storage which restricts us from only storing the information based on the
hardware constraints. Secondly, the computation required to process this data
is highly expensive which makes it infeasible to work on them. Compressive
sensing(CS)[2] is a signal process technique[11], through optimization, the
sparsity of a signal can be exploited to recover it from far fewer samples than
required by the Shannon-Nyquist sampling theorem. There are two conditions
under which recovery is possible. The first one is sparsity which requires the
signal to be sparse in some domain. The second one is incoherence which is
applied through the isometric property which is sufficient for sparse
signals[9][10]. To sustain these characteristics, preserving all attributes in
the uncompressed domain would help any kind of in this field. However, existing
dataset fallback in terms of continuous tracking of all the object present in
the scene, very few video datasets have comprehensive continuous tracking of
objects. To address these problems collectively, in this work we propose a new
comprehensive video dataset, where the data is compressed using pixel-wise
coded exposure [3] that resolves various other impediments.
