  Accurate pedestrian classification and localization have received
considerable attention due to their wide applications such as security
monitoring, autonomous driving, etc. Although pedestrian detectors have made
great progress in recent years, the fixed Intersection over Union (IoU) based
assignment-regression manner still limits their performance. Two main factors
are responsible for this: 1) the IoU threshold faces a dilemma that a lower one
will result in more false positives, while a higher one will filter out the
matched positives; 2) the IoU-based GT-Proposal assignment suffers from the
inconsistent supervision problem that spatially adjacent proposals with similar
features are assigned to different ground-truth boxes, which means some very
similar proposals may be forced to regress towards different targets, and thus
confuses the bounding-box regression when predicting the location results. In
this paper, we first put forward the question that \textbf{Regression
Direction} would affect the performance for pedestrian detection. Consequently,
we address the weakness of IoU by introducing one geometric sensitive search
algorithm as a new assignment and regression metric. Different from the
previous IoU-based \textbf{one-to-one} assignment manner of one proposal to one
ground-truth box, the proposed method attempts to seek a reasonable matching
between the sets of proposals and ground-truth boxes. Specifically, we boost
the MR-FPPI under R$_{75}$ by 8.8\% on Citypersons dataset. Furthermore, by
incorporating this method as a metric into the state-of-the-art pedestrian
detectors, we show a consistent improvement.
