Abstract: Video coding algorithms encode and decode an entire video frame while feature
coding techniques only preserve and communicate the most critical information
needed for a given application. This is because video coding targets human
perception, while feature coding aims for machine vision tasks. Recently,
attempts are being made to bridge the gap between these two domains. In this
work, we propose a video coding framework by leveraging on to the commonality
that exists between human vision and machine vision applications using cuboids.
This is because cuboids, estimated rectangular regions over a video frame, are
computationally efficient, has a compact representation and object centric.
Such properties are already shown to add value to traditional video coding
systems. Herein cuboidal feature descriptors are extracted from the current
frame and then employed for accomplishing a machine vision task in the form of
object detection. Experimental results show that a trained classifier yields
superior average precision when equipped with cuboidal features oriented
representation of the current test frame. Additionally, this representation
costs 7% less in bit rate if the captured frames are need be communicated to a