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Occlusions of objects is one of the indispensable problems in Computervision. While Convolutional Neural Net-works (CNNs) provide various state ofthe art approaches for regular image classification, they however, prove to benot as effective for the classification of images with partial occlusions.Partial occlusion is scenario where an object is occluded partially by someother object/space. This problem when solved,holds tremendous potential tofacilitate various scenarios. We in particular are interested in autonomousdriving scenario and its implications in the same. Autonomous vehicle researchis one of the hot topics of this decade, there are ample situations of partialocclusions of a driving sign or a person or other objects at different angles.Considering its prime importance in situations which can be further extended tovideo analytics of traffic data to handle crimes, anticipate income levels ofvarious groups etc.,this holds the potential to be exploited in many ways. Inthis paper, we introduce our own synthetically created dataset by utilisingStanford Car Dataset and adding occlusions of various sizes and nature to it.On this created dataset, we conducted a comprehensive analysis using variousstate of the art CNN models such as VGG-19, ResNet 50/101, GoogleNet, DenseNet121. We further in depth study the effect of varying occlusion proportions andnature on the performance of these models by fine tuning and training thesefrom scratch on dataset and how is it likely to perform when trained indifferent scenarios, i.e., performance when training with occluded images andunoccluded images, which model is more robust to partial occlusions and soon.

Now You See Me: Robust approach to Partial Occlusions