Pedestrian detection has been a hot spot in computer vision over the past
decades due to the wide spectrum of promising applications, the major challenge
of which is False Positives (FPs) that occur during pedestrian detection. The
emergence various Convolutional Neural Network-based detection strategies
substantially enhance the pedestrian detection accuracy but still not well
solve this problem. This paper deeply analysis the detection framework of the
two-stage CNN detection methods and find out false positives in detection
results is due to its training strategy miss classify some false proposals,
thus weakens the classification capability of following subnetwork and hardly
to suppress false ones. To solve this problem, This paper proposes a
pedestrian-sensitive training algorithm to effectively help two-stage CNN
detection methods learn to distinguish the pedestrian and non-pedestrian
samples and suppress the false positives in final detection results. The core
of the proposed training algorithm is to redesign the training proposal
generating pipeline of the two-stage CNN detection methods, which can avoid a
certain number of false ones that mislead its training process. With the help
of the proposed algorithm, the detection accuracy of the MetroNext, an smaller
and accurate metro passenger detector, is further improved, which further
decreases false ones in its metro passengers detection results. Based on
various challenging benchmark datasets, experiment results have demonstrated
that feasibility of the proposed algorithm to improve pedestrian detection
accuracy by removing the false positives. Compared with the competitors,
MetroNext-PST demonstrates better overall prediction performance in accuracy,
total number of parameters, and inference time, thus it can become a practical
solution for hunting pedestrian tailored for mobile and edge devices.

通过重新设计两阶段卷积神经网络检测方法的训练提议生成流程，本文提出了一种行人敏感的训练算法，用于帮助学习区分行人和非行人样本，并抑制最终检测结果中的伪阳性。实验证明了该算法提高了行人检测的准确性，与竞争者相比，MetroNext-PST 在准确性、参数总数和推理时间等方面表现更好，因此可成为专为移动和边缘设备定制的实用解决方案。