Abstract
Pedestrian detection has significantly progressed in recent years, thanks to the development of DNNs. However, detection performance at occluded scenes is still far from satisfactory, as occlusion increases the intra-class variance of pedestrians, hindering the model from finding an accurate classification boundary between pedestrians and background clutters. From the perspective of reducing intra-class variance, we propose to complete features for occluded regions so as to align the features of pedestrians across different occlusion patterns. An important premise for feature completion is to locate occluded regions. From our analysis, channel features of different pedestrian proposals only show high correlation values at visible parts and thus feature correlations can be used to model occlusion patterns. In order to narrow down the gap between completed features and real fully visible ones, we propose an adversarial learning method, which completes occluded features with a generator such that they can hardly be distinguished by the discriminator from real fully visible features. We report experimental results on the CityPersons, Caltech and CrowdHuman datasets. On CityPersons, we show significant improvements over five different baseline detectors, especially on the heavy occlusion subset. Furthermore, we show that our proposed method FeatComp++ achieves state-of-the-art results on all the above three datasets without relying on extra cues.
Abstract (translated)
行人检测在近年来取得了显著的进步，得益于深度学习网络的发展。然而，在遮挡场景中的检测性能仍然离满意尚较远，因为遮挡增加了行人的内类方差，阻碍了模型在行人与背景混淆之间找到精确分类边界。从减少内类方差的角度来看，我们提出了一种方法来完成遮挡区域的特征，以使不同遮挡模式下的行人特征对齐。完成特征的一个重要前提是找到遮挡区域。从我们的分析中，不同行人建议的通道特征仅在可见部分表现出高相关性，因此特征相关性可用于建模遮挡模式。为了缩小完成特征与真实完全可见特征之间的差距，我们提出了一个对抗学习方法，该方法使用生成器完成遮挡特征，这样它们很难被鉴别器与真实完全可见特征区分开来。我们在CityPersons、Caltech和CrowdHuman数据集上进行了实验。在CityPersons数据集上，我们展示了五种不同的基线检测器中显著的改进，特别是在重度遮挡子集中。此外，我们还证明了我们的方法FeatureComp++在三个数据集上均实现了最先进的成果，而无需依赖额外的提示。
URL
https://arxiv.org/abs/2405.01311
