Convnets have enabled significant progress in pedestrian detection recently,
but there are still open questions regarding suitable architectures and
training data. We revisit CNN design and point out key adaptations, enabling
plain FasterRCNN to obtain state-of-the-art results on the Caltech dataset.
To achieve further improvement from more and better data, we introduce
CityPersons, a new set of person annotations on top of the Cityscapes dataset.
The diversity of CityPersons allows us for the first time to train one single
CNN model that generalizes well over multiple benchmarks. Moreover, with
additional training with CityPersons, we obtain top results using FasterRCNN on
Caltech, improving especially for more difficult cases (heavy occlusion and
small scale) and providing higher localization quality.

本文通过重新评估 CNN 设计和引入新的数据集 CityPersons，使得 FasterRCNN 在 Caltech 数据集上取得了最新的结果，特别是在难度较大的情况下有了更好的表现，提供更高的定位质量。

CityPersons: 一个用于行人检测的多样化数据集

CityPersons: A Diverse Dataset for Pedestrian Detection

A new, radical CNN design approach is presented in this paper, considering
the reduction of the total computational load during inference. This is
achieved by a new holistic intervention on both the CNN architecture and the
training procedure, which targets to the parsimonious inference by learning to
exploit or remove the redundant capacity of a CNN architecture. This is
accomplished, by the introduction of a new structural element that can be
inserted as an add-on to any contemporary CNN architecture, whilst preserving
or even improving its recognition accuracy. Our approach formulates a
systematic and data-driven method for developing CNNs that are trained to
eventually change size and form in real-time during inference, targeting to the
smaller possible computational footprint. Results are provided for the optimal
implementation on a few modern, high-end mobile computing platforms indicating
a significant speed-up of up to x3 times.

本文提出了一种新的 CNN 设计方法，旨在通过对 CNN 架构和训练过程的综合干预来减少总推理计算量，通过引入一种新的结构元素作为附加组件到任何现代 CNN 架构中，系统地提出了一种数据驱动的开发 CNN 的公式，旨在实时改变大小和形式，从而使计算占用更小。最佳实现的结果表明了在几个现代高端移动计算平台上的显著加速。