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Layered Data Association Using Graph-Theoretic Formulation with Application to Tennis Ball Tracking in Monocular Sequences
- Source :
- IEEE Transactions on Pattern Analysis and Machine Intelligence. 30:1814-1830
- Publication Year :
- 2008
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2008.
-
Abstract
- In this paper, we propose a multi-layered data association scheme with graph-theoretic formulation for tracking multiple objects that undergo switching dynamics in clutter. The proposed scheme takes as input object candidates detected in each frame. At the object candidate level, "tracklets" are "grown" from sets of candidates that have high probabilities of containing only true positives. At the tracklet level, a directed and weighted graph is constructed, where each node is a tracklet, and the edge weight between two nodes is defined according to the "compatibility'' of the two tracklets. The association problem is then formulated as an all-pairs shortest path (APSP) problem in this graph. Finally, at the path level, by analyzing the all-pairs shortest paths, all object trajectories are identified, and track initiation and track termination are automatically dealt with. By exploiting a special topological property of the graph, we have also developed a more efficient APSP algorithm than the general-purpose ones. The proposed data association scheme is applied to tennis sequences to track tennis balls. Experiments show that it works well on sequences where other data association methods perform poorly or fail completely.
- Subjects :
- Video Recording
Sensitivity and Specificity
Pattern Recognition, Automated
Sports Equipment
Motion
Imaging, Three-Dimensional
Artificial Intelligence
Image Interpretation, Computer-Assisted
Mathematics
business.industry
Applied Mathematics
Reproducibility of Results
Graph theory
Directed graph
Topological graph
Image Enhancement
Object detection
Association scheme
Computational Theory and Mathematics
Tennis
Shortest path problem
Graph (abstract data type)
Tennis ball
Computer Vision and Pattern Recognition
Artificial intelligence
business
Algorithms
Software
Subjects
Details
- ISSN :
- 01628828
- Volume :
- 30
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Accession number :
- edsair.doi.dedup.....176efbd3088cefd6e848f19df82dee62