dstl/Stone-Soup
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
Algorithm ImplementationBelief PropagationData AssociationMulti-Target TrackingProbabilistic Data AssociationSimulation
Belmekki
PROFILE
Belmekki
Belamir developed a demonstration of Joint Probabilistic Data Association (JPDA) with Loopy Belief Propagation (LBP) for the dstl/Stone-Soup repository, focusing on scalable data association in multi-target tracking scenarios. Using Python, Belamir implemented a graphical model, message-passing logic, and belief calculation within the JPDA filter, enabling comparison between LBP-based data association and traditional enumeration methods. The demo simulated ground truth and measurements to illustrate computational complexity trade-offs, providing a practical framework for evaluating probabilistic data association techniques. This work showcased depth in algorithm implementation and simulation, laying a foundation for more efficient JPDA workflows and supporting exploratory analysis for users.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
364
Activity Months1
Your Network
31 peopleSame Organization
@liverpool.ac.uk2
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary: Delivered a JPDA Data Association with Loopy Belief Propagation (LBP) Demo in dstl/Stone-Soup to illustrate scalable data association in multi-object tracking. The demo provides a graphical model, message-passing logic, belief calculation, and a comparison against exact enumeration, using synthetic ground truth and measurements to demonstrate potential reductions in computational complexity. This work lays groundwork for more scalable JPDA workflows and accelerates exploratory analysis for internal teams and customers.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary: Delivered a JPDA Data Association with Loopy Belief Propagation (LBP) Demo in dstl/Stone-Soup to illustrate scalable data association in multi-object tracking. The demo provides a graphical model, message-passing logic, belief calculation, and a comparison against exact enumeration, using synthetic ground truth and measurements to demonstrate potential reductions in computational complexity. This work lays groundwork for more scalable JPDA workflows and accelerates exploratory analysis for internal teams and customers.
May 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Algorithm ImplementationBelief PropagationData AssociationMulti-Target TrackingProbabilistic Data AssociationSimulation
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