论文标题
改编的中心和规模预测：更稳定，更准确
Adapted Center and Scale Prediction: More Stable and More Accurate
论文作者
论文摘要
近年来，行人检测受益于深度学习技术，并获得了快速发展。大多数检测器都遵循一般对象检测框架，即默认框和两个阶段过程。最近，已引入了无锚定和一阶段探测器。但是，它们的准确性并不令人满意。因此，为了享受无锚探测器的简单性以及同时享受两阶段的探测器的准确性，我们提出了一些基于检测器，中心和比例预测（CSP）的改编。我们论文的主要贡献是：（1）我们提高了CSP的鲁棒性并使训练更容易。 （2）我们提出了一种预测宽度的新方法，即压缩宽度。 （3）我们在CityPersons基准上取得了第二好的性能，即合理设置的9.3％的对数平均损失率（MR），部分套装的MR 8.7％，Bare Set的MR 5.6％，这表明无锚定和一阶段的探测器仍然可以具有很高的精度。 （4）我们探讨了一些可切换归一化的功能，而原始论文中未提及这些功能。
Pedestrian detection benefits from deep learning technology and gains rapid development in recent years. Most of detectors follow general object detection frame, i.e. default boxes and two-stage process. Recently, anchor-free and one-stage detectors have been introduced into this area. However, their accuracies are unsatisfactory. Therefore, in order to enjoy the simplicity of anchor-free detectors and the accuracy of two-stage ones simultaneously, we propose some adaptations based on a detector, Center and Scale Prediction(CSP). The main contributions of our paper are: (1) We improve the robustness of CSP and make it easier to train. (2) We propose a novel method to predict width, namely compressing width. (3) We achieve the second best performance on CityPersons benchmark, i.e. 9.3% log-average miss rate(MR) on reasonable set, 8.7% MR on partial set and 5.6% MR on bare set, which shows an anchor-free and one-stage detector can still have high accuracy. (4) We explore some capabilities of Switchable Normalization which are not mentioned in its original paper.
