44 citations · 153 across the 10 of their papers we have counts for
11 papers
TraHist: Fast and Accurate Cardinality Estimation for Trajectory Data (Extended Version)
Yujeong Jo, Incheol Baek, Changbeom Shim +1
Cardinality estimation is fundamental for query optimization in database systems. Histogram-based estimators are widely used for their simplicity and efficiency but rely on point-c…
The Mean of Multi-Object Trajectories
Tran Thien Dat Nguyen, Ba Tuong Vo, Ba-Ngu Vo +2
This paper introduces the concept of a mean for trajectories and multi-object trajectories (defined as sets or multi-sets of trajectories) along with algorithms for computing them.…
Property Estimation in Geotechnical Databases Using Labeled Random Finite Sets
Changbeom Shim, Youngho Kim, Craig Butterworth
The sufficiency of accurate data is a core element in data-centric geotechnics. However, geotechnical datasets are essentially uncertain, whereupon engineers have difficulty with o…
An Overview of Multi-Object Estimation via Labeled Random Finite Set
Ba-Ngu Vo, Ba-Tuong Vo, Tran Thien Dat Nguyen +1
This article presents the Labeled Random Finite Set (LRFS) framework for multi-object systems-systems in which the number of objects and their states are unknown and vary randomly…
Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets
Linh Van Ma, Tran Thien Dat Nguyen, Changbeom Shim +3
This paper proposes an online visual multi-object tracking (MOT) algorithm that resolves object appearance-reappearance and occlusion. Our solution is based on the labeled random f…
The Smooth Trajectory Estimator for LMB Filters
Hoa Van Nguyen, Tran Thien Dat Nguyen, Changbeom Shim +1
This paper proposes a smooth-trajectory estimator for the labelled multi-Bernoulli (LMB) filter by exploiting the special structure of the generalised labelled multi-Bernoulli (GLM…
Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning
Ji Youn Lee, Changbeom Shim, Hoa Van Nguyen +3
Estimating the trajectories of multi-objects poses a significant challenge due to data association ambiguity, which leads to a substantial increase in computational requirements. T…
Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering
Changbeom Shim, Ba-Tuong Vo, Ba-Ngu Vo +2
Generalized Labeled Multi-Bernoulli (GLMB) densities arise in a host of multi-object system applications analogous to Gaussians in single-object filtering. However, computing the G…
A Moving Window Based Approach to Multi-scan Multi-Target Tracking
Diluka Moratuwage, Changbeom Shim, Yuthika Punchihewa
Multi-target state estimation refers to estimating the number of targets and their trajectories in a surveillance area using measurements contaminated with noise and clutter. In th…
Multi-Scan Multi-Sensor Multi-Object State Estimation
D. Moratuwage, B. -N. Vo, B. -T. Vo +1
If computational tractability were not an issue, multi-object estimation should integrate all measurements from multiple sensors across multiple scans. In this article, we propose…
Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli Filter
Cong-Thanh Do, Tran Thien Dat Nguyen, Diluka Moratuwage +2
The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process. Further, the information on loc…