Object detection generally requires sliding-window classifiers in tradition
or anchor box based predictions in modern deep learning approaches. However,
either of these approaches requires tedious configurations in boxes. In this
paper, we provide a new perspective where detecting objects is motivated as a
high-level semantic feature detection task. Like edges, corners, blobs and
other feature detectors, the proposed detector scans for feature points all
over the image, for which the convolution is naturally suited. However, unlike
these traditional low-level features, the proposed detector goes for a
higher-level abstraction, that is, we are looking for central points where
there are objects, and modern deep models are already capable of such a
high-level semantic abstraction. Besides, like blob detection, we also predict
the scales of the central points, which is also a straightforward convolution.
Therefore, in this paper, pedestrian and face detection is simplified as a
straightforward center and scale prediction task through convolutions. This
way, the proposed method enjoys a box-free setting. Though structurally simple,
it presents competitive accuracy on several challenging benchmarks, including
pedestrian detection and face detection. Furthermore, a cross-dataset
evaluation is performed, demonstrating a superior generalization ability of the
proposed method. Code and models can be accessed at
(this https URL and this https URL).

本文提出了一种新的视角，即通过高级语义特征检测任务来检测对象，通过卷积实现中心和比例预测来简化行人和人脸检测，使其无需使用窗口分类器或基于锚箱的预测方式，并展现了在多项挑战性基准测试中具有竞争力的准确性以及卓越的泛化能力。