While visual object detection with deep learning has received much attention
in the past decade, cases when heavy intra-class occlusions occur have not been
studied thoroughly. In this work, we propose a Non-Maximum-Suppression (NMS)
algorithm that dramatically improves the detection recall while maintaining
high precision in scenes with heavy occlusions. Our NMS algorithm is derived
from a novel embedding mechanism, in which the semantic and geometric features
of the detected boxes are jointly exploited. The embedding makes it possible to
determine whether two heavily-overlapping boxes belong to the same object in
the physical world. Our approach is particularly useful for car detection and
pedestrian detection in urban scenes where occlusions often happen. We show the
effectiveness of our approach by creating a model called SG-Det (short for
Semantics and Geometry Detection) and testing SG-Det on two widely-adopted
datasets, KITTI and CityPersons for which it achieves state-of-the-art
performance.

本文提出一种名为 SG-Det 的模型，使用一种新颖的嵌入机制来联合利用检测框的语义和几何特征，从而在面对类内遮挡较多的场景时显著提高了检测召回率，特别适用于城市场景的汽车和行人检测，在 KITTI 和 CityPersons 数据集上展示了 SG-Det 模型的最优性能。