no code implementations • 29 Apr 2025 • Tran Thien Dat Nguyen, Ba Tuong Vo, Ba-Ngu Vo, Hoa Van Nguyen, Changbeom Shim
This paper introduces the concept of a mean for trajectories and multi-object trajectories--sets or multi-sets of trajectories--along with algorithms for computing them.
no code implementations • 27 Sep 2024 • Ba-Ngu Vo, Ba-Tuong Vo, Tran Thien Dat Nguyen, Changbeom Shim
Building on this, we outline the fundamentals of multi-object state space modeling and estimation using LRFS, which formally address object identities/trajectories, ancestries for spawning objects, and characterization of the uncertainty on the ensemble of objects (and their trajectories).
3 code implementations • 28 May 2024 • Linh Van Ma, Tran Thien Dat Nguyen, Ba-Ngu Vo, Hyunsung Jang, Moongu Jeon
Specifically, we exploit the 2D detections and extracted features from multiple cameras to provide a better approximation of the multi-object filtering density to realize the track initiation/termination and re-identification functionalities.
no code implementations • 29 Nov 2022 • Changbeom Shim, Ba-Tuong Vo, Ba-Ngu Vo, Jonah Ong, Diluka Moratuwage
Specifically, we propose a tempered Gibbs sampler that exploits the structure of the GLMB filtering density to achieve an $\mathcal{O}(T(P+M))$ complexity, where $T$ is the number of iterations of the algorithm, $P$ and $M$ are the number hypothesized objects and measurements.
1 code implementation • 22 Apr 2021 • Tran Thien Dat Nguyen, Ba-Ngu Vo, Ba-Tuong Vo, Du Yong Kim, Yu Suk Choi
Determining the trajectories of cells and their lineages or ancestries in live-cell experiments are fundamental to the understanding of how cells behave and divide.
1 code implementation • 8 Aug 2020 • Tran Thien Dat Nguyen, Hamid Rezatofighi, Ba-Ngu Vo, Ba-Tuong Vo, Silvio Savarese, Ian Reid
This paper examines performance evaluation criteria for basic vision tasks involving sets of objects namely, object detection, instance-level segmentation and multi-object tracking.
no code implementations • 27 Mar 2017 • Quang N. Tran, Ba-Ngu Vo, Dinh Phung, Ba-Tuong Vo, Thuong Nguyen
Multiple instance data are sets or multi-sets of unordered elements.
no code implementations • 7 Mar 2017 • Ba-Ngu Vo, Dinh Phung, Quang N. Tran, Ba-Tuong Vo
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.
no code implementations • 8 Feb 2017 • Quang N. Tran, Ba-Ngu Vo, Dinh Phung, Ba-Tuong Vo
However, there has been limited research in the clustering of point patterns - sets or multi-sets of unordered elements - that are found in numerous applications and data sources.
no code implementations • 30 Jan 2017 • Ba-Ngu Vo, Quang N. Tran, Dinh Phung, Ba-Tuong Vo
Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources.
no code implementations • 18 Nov 2016 • Du Yong Kim, Ba-Ngu Vo, Ba-Tuong Vo
Furthermore the labeled random finite set framework enables the incorporation of prior knowledge that mis-detections of long tracks which occur in the middle of the scene are likely to be due to occlusions.
no code implementations • 23 Jul 2015 • Seyed Hamid Rezatofighi, Stephen Gould, Ba Tuong Vo, Ba-Ngu Vo, Katarina Mele, Richard Hartley
To deal with this, we propose a bootstrap filter composed of an estimator and a tracker.
no code implementations • 2 Jun 2015 • Hung Gia Hoang, Ba-Tuong Vo, Ba-Ngu Vo
This paper proposes an efficient implementation of the generalized labeled multi-Bernoulli (GLMB) filter by combining the prediction and update into a single step.