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

TL;DR

This paper introduces a novel pedestrian detection method combining somatic topological line localization and temporal feature aggregation, significantly improving detection of small-scale pedestrians in videos.

Contribution

The paper proposes a new approach integrating TLL and temporal features, along with MRF-based post-processing, to enhance small-scale pedestrian detection and address annotation bias.

Findings

Achieved best detection performance on Caltech benchmark.

Reduced small-scale pedestrian miss rate from 74.53% to 60.79%.

Demonstrated annotation bias in KITTI dataset.

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.