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.

翻译：物体遮挡是计算机视觉中亟待解决的关键问题之一。尽管卷积神经网络（CNN）在常规图像分类中提供了多种先进方法，但在处理部分遮挡图像分类时却效果欠佳。部分遮挡是指物体被其他物体或空间区域部分遮挡的情况。该问题的解决在诸多场景中具有巨大潜力，我们特别关注其在自动驾驶场景中的应用与影响。作为当前的研究热点，自动驾驶技术常面临交通标识、行人及其他物体在不同角度下的部分遮挡情况。鉴于该问题在交通数据视频分析（可延伸应用于犯罪侦查、群体收入水平预测等领域）中的核心地位，其应用价值值得深入挖掘。本文基于斯坦福汽车数据集引入自主合成数据集，通过添加不同尺寸和特征类型的遮挡区域构建实验样本。我们采用VGG-19、ResNet 50/101、GoogleNet、DenseNet 121等先进CNN模型进行系统分析，深入研究了不同遮挡比例与特征类型对模型性能的影响。通过微调及从零训练的方式，对比分析了不同训练场景（如遮挡图像与无遮挡图像的训练组合）下的模型表现，探讨了各模型对部分遮挡的鲁棒性差异。