  A critical issue in pedestrian detection is to detect small-scale objects
that will introduce feeble contrast and motion blur in images and videos, which
in our opinion should partially resort to deep-rooted annotation bias.
Motivated by this, we propose a novel method integrated with somatic
topological line localization (TLL) and temporal feature aggregation for
detecting multi-scale pedestrians, which works particularly well with
small-scale pedestrians that are relatively far from the camera. Moreover, a
post-processing scheme based on Markov Random Field (MRF) is introduced to
eliminate ambiguities in occlusion cases. Applying with these methodologies
comprehensively, we achieve best detection performance on Caltech benchmark and
improve performance of small-scale objects significantly (miss rate decreases
from 74.53% to 60.79%). Beyond this, we also achieve competitive performance on
CityPersons dataset and show the existence of annotation bias in KITTI dataset.
