Paper Info
Title
Multiple human tracking system for unpredictable trajectories
Abstract
Tracking multiple objects into a scene is one of the most active research topics in computer vision. The art of identifying each target within the scene along a video sequence has multiple issues to be solved, being collision and occlusion events among the most challenging ones. Because of this, when dealing with human detection, it is often very difficult to obtain a full body image, which introduces complexity in the process. The task becomes even more difficult when dealing with unpredictable trajectories, like in sport environments. Thus, head-shoulder omega shape becomes a powerful tool to perform the human detection. Most of the contributions to this field involve a detection technique followed by a tracking system based on the omega-shape features. Based on these works, we present a novel methodology for providing a full tracking system. Different techniques are combined to both detect, track and recover target identifications under unpredictable trajectories, such as sport events. Experimental results into challenging sport scenes show the performance and accuracy of this technique. Also, the system speed opens the door for obtaining a real-time system using GPU programing in standard desktop machines, being able to be used in higher-level human behavioral systems, with multiple applications.
Year
DOI
Venue
2014
10.1007/s00138-013-0544-7
Mach. Vis. Appl.
Keywords
Field
DocType
Background subtraction,Cascade classifier,Histogram of oriented gradients,Particle filter,Collision detection,Occlusion recovery
Background subtraction,Computer vision,Collision detection,Pattern recognition,Computer science,Cascading classifiers,Particle filter,Tracking system,Collision,Histogram of oriented gradients,Artificial intelligence
Journal
Volume
Issue
ISSN
25
2
0932-8092
Citations 
PageRank 
References 
4
0.42
22
Authors
3
Name
Order
Citations
PageRank
Brais Cancela1669.19
M Ortega223537.13
Manuel G. Penedo328424.93