Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle

TL;DR

This paper discusses the challenges of pedestrian sensing from moving vehicles, compares image and LiDAR methods, and suggests that specialized benchmarks could enhance traffic safety research.

Contribution

It highlights the need for traffic data-specific benchmarks for articulated human detection using LiDAR and discusses the limitations of current methods in onboard pedestrian sensing.

Findings

LiDAR methods lack in articulated human detection at a distance

Onboard video quality is affected by motion blur and occlusions

Traffic-specific benchmarks could improve pedestrian sensing research

Abstract

It is difficult to perform 3D reconstruction from on-vehicle gathered video due to the large forward motion of the vehicle. Even object detection and human sensing models perform significantly worse on onboard videos when compared to standard benchmarks because objects often appear far away from the camera compared to the standard object detection benchmarks, image quality is often decreased by motion blur and occlusions occur often. This has led to the popularisation of traffic data-specific benchmarks. Recently Light Detection And Ranging (LiDAR) sensors have become popular to directly estimate depths without the need to perform 3D reconstructions. However, LiDAR-based methods still lack in articulated human detection at a distance when compared to image-based methods. We hypothesize that benchmarks targeted at articulated human sensing from LiDAR data could bring about increased research in human sensing and prediction in traffic and could lead to improved traffic safety for pedestrians.