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

Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation