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

Tao Song, Leiyu Sun, Di Xie, Haiming Sun, Shiliang Pu; The European Conference on Computer Vision (ECCV), 2018, pp. 536-551

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 Ran- dom 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 signicantly (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.

Related Material


[pdf]
[bibtex]
@InProceedings{Song_2018_ECCV,
author = {Song, Tao and Sun, Leiyu and Xie, Di and Sun, Haiming and Pu, Shiliang},
title = {Small-scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}