A Novel Data Association Algorithm for Object Tracking in Clutter with Application to Tennis Video Analysis

Authors:
Fei Yan;Alexey Kostin;William Christmas;Josef Kittler
Affiliations:
University of Surrey, UK;University of Surrey, UK;University of Surrey, UK;University of Surrey, UK
Venue:
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Year:
2006

Citing 0
Cited 4

A novel approach for tracking high speed skaters in sports using a panning camera

Pattern Recognition
A three-level scheme for real-time ball tracking

MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Object Detection and Tracking for Autonomous Navigation in Dynamic Environments

International Journal of Robotics Research
Automated stroke classification in Tennis

ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is well recognised that data association is critically important for object tracking. However, in the presence of successive misdetections, a large number of false candidates and an unknown number of abrupt model switchings that happen unpredictably, the data association problem can be very difficult. We tackle these difficulties by using a layered data association scheme. At the object level, trajectories are "grown" from sets of object candidates that have high probabilities of containing only true positives; by this means the otherwise combinatorial complexity is significantly reduced. Dijkstra's shortest path algorithm is then used to perform data association at the trajectory level. The algorithm is applied to low-quality tennis video sequences to track a tennis ball. Experiments show that the algorithm is robust to abrupt model switchings, and performs well in heavily cluttered environments.