Description: Implementation of the Murty algorithm to obtain the best K assignments. Includes the implementation of the Jonker-Volgenant algorithm.
Usual applications are multiple target tracking algorithms, Joint Probabilistic Data Association (JPDA), Multiple Hypothesis Tracking (MHT), Global Nearest Neighbours (GNN), etc.
Based on the original code by Jonker-Volgenant, and the Murty Matlab implementation by Eric Trautmann
Can be used both from Java or Matlab.
Time for a 100x100 assignment problem, with k=20:
• Matlab implementation: 54.5 sec
• Java implementation: 1 sec
-Implementation of the Murty algorithm to obtain the best K assignments. Includes the implementation of the Jonker-Volgenant algorithm.
Usual applications are multiple target tracking algorithms, Joint Probabilistic Data Association (JPDA), Multiple Hypothesis Tracking (MHT), Global Nearest Neighbours (GNN), etc.
Based on the original code by Jonker-Volgenant, and the Murty Matlab implementation by Eric Trautmann
Can be used both from Java or Matlab.
Time for a 100x100 assignment problem, with k=20:
• Matlab implementation: 54.5 sec
• Java implementation: 1 sec
Platform: |
Size: 27648 |
Author:hansen |
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