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Found 11 papers, 6 papers with code
The Mean of Multi-Object Trajectories
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.
An Overview of Multi-Object Estimation via Labeled Random Finite Set
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).
Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets
In occlusion handling, the filter's efficacy is dictated by trade-offs between the sophistication of the occlusion model and computational demand.
Track Initialization and Re-Identification for~3D Multi-View Multi-Object Tracking
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.
The Smooth Trajectory Estimator for LMB Filters
In particular, we construct a smooth-trajectory estimator (i. e., an estimator over the entire trajectories of labelled estimates) for the LMB filter based on the history of the best association map and all of the measurements up to the current time.
Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning
To address such problems, a divide-and-conquer manner has been employed with parallel computation.
Robust multi-sensor Generalized Labeled Multi-Bernoulli filter
This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown target detection profiles and clutter rates using measurements from multiple sensors.
Tracking Cells and their Lineages via Labeled Random Finite Sets
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.
How Trustworthy are Performance Evaluations for Basic Vision Tasks?
This paper examines performance evaluation criteria for basic vision tasks involving sets of objects namely, object detection, instance-level segmentation and multi-object tracking.
Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli Filter
The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process.
GLMB Tracker with Partial Smoothing
In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements.
