Paper Info
Title
RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety
Abstract
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.
Year
DOI
Venue
2020
10.1007/978-3-030-68787-8_47
ICPR Workshops
DocType
ISSN
Citations 
Conference
Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12667. Springer, Cham
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Marco Wallner100.34
Daniel Steininger202.03
Verena Widhalm300.34
Matthias Schörghuber400.34
Csaba Beleznai536718.96