  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.
