Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle
This work tackles the problem of improving pedestrian safety in traffic through better sensing, but it is incremental as it focuses on benchmarking rather than introducing a new method.
The paper addresses the challenge of articulated human detection from LiDAR data in moving vehicles, where current methods underperform compared to image-based approaches, and proposes that targeted benchmarks could enhance research and improve pedestrian traffic safety.
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.