1 citations · 1 across the 12 of their papers we have counts for
12 papers
Efficient Implementations of Extended Object PMBM Filters with Blocked Gibbs Sampling
Yuxuan Xia, Ángel F. García-Fernández, Lennart Svensson
This paper considers multiple extended object tracking based on Poisson multi-Bernoulli mixture (PMBM) filtering, which gives the closed-form Bayesian solution for standard multipl…
Occlusion-Aware Multi-Object Tracking via Expected Probability of Detection
Jan Krejčí, Oliver Kost, Yuxuan Xia +2
This paper addresses multi-object systems, where objects may occlude one another relative to the sensor. The standard point-object model for detection-based sensors is enhanced so…
Model-based Multi-object Visual Tracking: Identification and Standard Model Limitations
Jan Krejčí, Oliver Kost, Yuxuan Xia +2
This paper uses multi-object tracking methods known from the radar tracking community to address the problem of pedestrian tracking using 2D bounding box detections. The standard p…
GOSPA and T-GOSPA quasi-metrics for evaluation of multi-object tracking algorithms
Ángel F. García-Fernández, Jinhao Gu, Lennart Svensson +4
This paper introduces two quasi-metrics for performance assessment of multi-object tracking (MOT) algorithms. One quasi-metric is an extension of the generalised optimal subpattern…
Probabilistic Trajectory GOSPA: A Metric for Uncertainty-Aware Multi-Object Tracking Performance Evaluation
Yuxuan Xia, Ángel F. García-Fernández, Johan Karlsson +3
This paper presents a generalization of the trajectory general optimal sub-pattern assignment (GOSPA) metric for evaluating multi-object tracking algorithms that provide trajectory…
Probabilistic GOSPA: A Metric for Performance Evaluation of Multi-Object Filters with Uncertainties
Yuxuan Xia, Ángel F. García-Fernández, Johan Karlsson +3
This paper presents a probabilistic generalization of the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric, termed P-GOSPA. The GOSPA metric has been widely used to evalua…
TGOSPA Metric Parameters Selection and Evaluation for Visual Multi-object Tracking
Jan Krejčí, Oliver Kost, Ondřej Straka +3
Multi-object tracking algorithms are deployed in various applications, each with different performance requirements. For example, track switches pose significant challenges for off…
Target Handover in Distributed Integrated Sensing and Communication
Yu Ge, Ossi Kaltiokallio, Hui Chen +7
The concept of 6G distributed integrated sensing and communications (DISAC) builds upon the functionality of integrated sensing and communications (ISAC) by integrating distributed…
Hybrid PHD-PMB Trajectory Smoothing Using Backward Simulation
Yuxuan Xia, Ángel F. García-Fernández, Lennart Svensson
The probability hypothesis density (PHD) and Poisson multi-Bernoulli (PMB) filters are two popular set-type multi-object filters. Motivated by the fact that the multi-object filter…
Batch SLAM with PMBM Data Association Sampling and Graph-Based Optimization
Yu Ge, Ossi Kaltiokallio, Yuxuan Xia +6
Simultaneous localization and mapping (SLAM) methods need to both solve the data association (DA) problem and the joint estimation of the sensor trajectory and the map, conditioned…
Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing
Juliano Pinto, Georg Hess, Yuxuan Xia +2
Multi-object tracking (MOT) is the task of estimating the state trajectories of an unknown and time-varying number of objects over a certain time window. Several algorithms have be…
Markov Chain Monte Carlo Multi-Scan Data Association for Sets of Trajectories
Yuxuan Xia, Ángel F. García-Fernández, Lennart Svensson
This paper considers a batch solution to the multi-object tracking problem based on sets of trajectories. Specifically, we present two offline implementations of the trajectory Poi…