  Automated monitoring and analysis of passenger movement in safety-critical
parts of transport infrastructures represent a relevant visual surveillance
task. Recent breakthroughs in visual representation learning and spatial
sensing opened up new possibilities for detecting and tracking humans and
objects within a 3D spatial context. This paper proposes a flexible analysis
scheme and a thorough evaluation of various processing pipelines to detect and
track humans on a ground plane, calibrated automatically via stereo depth and
pedestrian detection. We consider multiple combinations within a set of RGB-
and depth-based detection and tracking modalities. We exploit the modular
concepts of Meshroom [2] and demonstrate its use as a generic vision processing
pipeline and scalable evaluation framework. Furthermore, we introduce a novel
open RGB-D railway platform dataset with annotations to support research
activities in automated RGB-D surveillance. We present quantitative results for
multiple object detection and tracking for various algorithmic combinations on
our dataset. Results indicate that the combined use of depth-based spatial
information and learned representations yields substantially enhanced detection
and tracking accuracies. As demonstrated, these enhancements are especially
pronounced in adverse situations when occlusions and objects not captured by
learned representations are present.
