772 citations · 2.1k across the 33 of their papers we have counts for
8 papers · 1 filter
Multiple Object Tracking in Unknown Backgrounds with Labeled Random Finite Sets
Yuthika Punchihewa, Ba-Tuong Vo, Ba-Ngu Vo +1
This paper proposes an on-line multiple object tracking algorithm that can operate in unknown background. In a majority of multiple object tracking applications, model parameters f…
A Generalized Labeled Multi-Bernoulli Filter with Object Spawning
Daniel S. Bryant, Ba Tuong Vo, Ba Ngu Vo +1
Previous labeled random finite set filter developments use a motion model that only accounts for survival and birth. While such a model provides the means for a multi-object tracki…
Multiple Instance Learning with the Optimal Sub-Pattern Assignment Metric
Quang N. Tran, Ba-Ngu Vo, Dinh Phung +2
Multiple instance data are sets or multi-sets of unordered elements. Using metrics or distances for sets, we propose an approach to several multiple instance learning tasks, such a…
A Random Finite Set Model for Data Clustering
Dinh Phung, Ba-Ngu Bo
The goal of data clustering is to partition data points into groups to minimize a given objective function. While most existing clustering algorithms treat each data point as vecto…
Model-Based Multiple Instance Learning
Ba-Ngu Vo, Dinh Phung, Quang N. Tran +1
While Multiple Instance (MI) data are point patterns -- sets or multi-sets of unordered points -- appropriate statistical point pattern models have not been used in MI learning. Th…
Multi-Sensor Multi-object Tracking with the Generalized Labeled Multi-Bernoulli Filter
Ba Ngu Vo, Ba Tuong Vo
This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update to…