Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Document Type
Event
Start Date
22-4-2026 4:00 PM
Description
Multi-Hypothesis Tracking (MHT) is a framework for solving the data association problem in multi-target tracking by maintaining multiple possible assignments between observations and targets over time. Rather than committing to a single solution, MHT explores a set of competing hypotheses, allowing it to handle noise, missed detections, and ambiguous measurements. In practical systems such as radar, LiDAR, and vision-based tracking, MHT is commonly implemented using algorithms like Murty’s algorithm to generate multiple high-quality assignment solutions from the Hungarian algorithm. In this work, we instead propose an assignment-tree-based approach, where hypotheses are incrementally constructed and prioritized using a structured search strategy. This allows for more flexible exploration and pruning of the hypothesis space compared to traditional k-best enumeration methods.
Included in
GRM-156-153 Finding Top-K assignments for Multi-Hypothesis Tracking
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Multi-Hypothesis Tracking (MHT) is a framework for solving the data association problem in multi-target tracking by maintaining multiple possible assignments between observations and targets over time. Rather than committing to a single solution, MHT explores a set of competing hypotheses, allowing it to handle noise, missed detections, and ambiguous measurements. In practical systems such as radar, LiDAR, and vision-based tracking, MHT is commonly implemented using algorithms like Murty’s algorithm to generate multiple high-quality assignment solutions from the Hungarian algorithm. In this work, we instead propose an assignment-tree-based approach, where hypotheses are incrementally constructed and prioritized using a structured search strategy. This allows for more flexible exploration and pruning of the hypothesis space compared to traditional k-best enumeration methods.