Small-scale Pedestrian Detection Based on
Topological Line Localization and Temporal
Feature Aggregation
Abstract. 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