Now You See Me: Robust approach to Partial Occlusions

Abstract

Object occlusion is one of the most indispensable and challenging problems in computer vision. While convolutional neural networks (CNNs) have proven to be effective for regular image classification, they struggle with images that have partial occlusions. Partial occlusion is a scenario where an object is partially occluded by another object or space. This problem, when solved, holds tremendous potential to facilitate various scenarios. We, in particular, are interested in the autonomous driving scenario and its implications. Autonomous vehicle research is one of the hot topics of this decade. There are ample situations of partial occlusions of a driving sign, a person, or other objects at different angles. The importance of this problem extends beyond autonomous driving and could be exploited for video analytics in traffic data to handle crimes, anticipate income levels of various groups, and more. In this paper, we introduce our own synthetically created dataset by utilizing the Stanford Car Dataset and adding occlusions of various sizes and natures to it. then conduct a comprehensive analysis using various state-of-the-art models and study the effect of varying occlusion proportions and nature on the models' performance. This research provides insights into which models are more robust to partial occlusions and how they perform when trained with occluded and unoccluded images, which can aid in developing more reliable autonomous driving systems and other computer vision applications.