RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety
This work addresses passenger safety in railway platforms through automated surveillance, but it is incremental as it builds on existing methods and datasets.
The paper tackled the problem of automated monitoring and analysis of passenger movement in railway platforms for enhanced safety, proposing a flexible analysis scheme and evaluating various RGB-D processing pipelines, with results showing that combining depth-based spatial information and learned representations substantially improves detection and tracking accuracies, especially in adverse conditions like occlusions.
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.