Paper Info
Title
Real-Time Multipedestrian Tracking in Traffic Scenes via an RGB-D-Based Layered Graph Model
Abstract
Multipedestrian tracking in traffic scenes is challenging due to cluttered backgrounds and serious occlusions. In this paper, we propose a layered graph model in image (RGB) and depth (D) domains for real-time robust multipedestrian tracking. The motivation is to investigate high-level constraints in RGB-D data association and to improve the optimization from the trajectory level to the layer level. To construct a layered graph, we define constraints in the depth domain so that pedestrian objects in the image domain are assigned to proper layers. We use pedestrian detection responses in the RGB domain as graph nodes, and we integrate 3-D motion, appearance, and depth features as graph edges. An online updating depth factor is defined to describe the depth relationships among the observations in and out of the layers, and the occlusion issue is processed with an analytical layer-level strategy. With a heuristic label switching algorithm, multiple pedestrian objects are optimally associated and tracked. Experiments and comparison on five public data sets show that our proposed approach significantly reduces pedestrian's ID switch and improves tracking accuracy in the cases of serious occlusions.
Year
DOI
Venue
2015
10.1109/TITS.2015.2423709
IEEE Trans. Intelligent Transportation Systems
Keywords
Field
DocType
Multi-pedestrian tracking, layered graph model, RGB-D data, occlusion
Data modeling,Computer vision,Data set,Heuristic,Simulation,Computer science,Feature extraction,Label switching,Artificial intelligence,RGB color model,Pedestrian detection,Trajectory
Journal
Volume
Issue
ISSN
PP
99
1524-9050
Citations 
PageRank 
References 
6
0.42
36
Authors
5
Name
Order
Citations
PageRank
Shan Gao180.78
Zhenjun Han217616.40
Ce Li3378.03
Qixiang Ye491364.51
Jianbin Jiao536732.61