Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
Multi-object tracking (MOT) supports applications such as radar monitoring and autonomous perception, where multiple objects move, appear, or disappear over time. A central challenge is resolving which detections correspond to which tracks. The Hungarian algorithm is often used to solve this assignment problem. For ambiguous scenes, Murty’s algorithm extends this approach by generating multiple top-k association hypotheses. In this work, we study an alternative search-space formulation for top-k enumeration. Our results show that it can provide strong speedups over Murty’s method on small matrices. We also reviewed identity-focused MOT evaluation metrics such as HOTA and created a visualization tool to examine how different association choices affect tracking accuracy.
Included in
GRM-0210 Distance Measures for Multi-Target Tracking
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Multi-object tracking (MOT) supports applications such as radar monitoring and autonomous perception, where multiple objects move, appear, or disappear over time. A central challenge is resolving which detections correspond to which tracks. The Hungarian algorithm is often used to solve this assignment problem. For ambiguous scenes, Murty’s algorithm extends this approach by generating multiple top-k association hypotheses. In this work, we study an alternative search-space formulation for top-k enumeration. Our results show that it can provide strong speedups over Murty’s method on small matrices. We also reviewed identity-focused MOT evaluation metrics such as HOTA and created a visualization tool to examine how different association choices affect tracking accuracy.