  Occlusions of objects is one of the indispensable problems in Computer
vision. While Convolutional Neural Net-works (CNNs) provide various state of
the art approaches for regular image classification, they however, prove to be
not as effective for the classification of images with partial occlusions.
Partial occlusion is scenario where an object is occluded partially by some
other object/space. This problem when solved,holds tremendous potential to
facilitate various scenarios. We in particular are interested in autonomous
driving scenario and its implications in the same. Autonomous vehicle research
is one of the hot topics of this decade, there are ample situations of partial
occlusions of a driving sign or a person or other objects at different angles.
Considering its prime importance in situations which can be further extended to
video analytics of traffic data to handle crimes, anticipate income levels of
various groups etc.,this holds the potential to be exploited in many ways. In
this paper, we introduce our own synthetically created dataset by utilising
Stanford Car Dataset and adding occlusions of various sizes and nature to it.
On this created dataset, we conducted a comprehensive analysis using various
state of the art CNN models such as VGG-19, ResNet 50/101, GoogleNet, DenseNet
121. We further in depth study the effect of varying occlusion proportions and
nature on the performance of these models by fine tuning and training these
from scratch on dataset and how is it likely to perform when trained in
different scenarios, i.e., performance when training with occluded images and
unoccluded images, which model is more robust to partial occlusions and soon.
