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Found 30 papers, 15 papers with code
LiDAR Point Cloud-based Multiple Vehicle Tracking with Probabilistic Measurement-Region Association
Simulation results illustrate the superior estimation accuracy of the proposed PMRA-PMBM filter in terms of both positions and extents of the vehicles comparing with PMBM filters using the gamma Gaussian inverse Wishart and DRA implementations.
Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention
On the other hand, the large visual difference between query and support images may hinder knowledge transfer and cripple the segmentation performance.
Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing
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.
Markov Chain Monte Carlo Data Association for Sets of Trajectories
This paper considers a batch solution to the multi-object tracking problem based on sets of trajectories.
V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges
To address this gap, this paper provides a comprehensive overview of the evolution of CP technologies, spanning from early explorations to recent developments, including advancements in V2X communication technologies.
Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar?
These provide the first benchmark and important insights for the future development of 4D imaging radar-based online 3D MOT algorithms.
LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds
The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role.
Scene Separation & Data Selection: Temporal Segmentation Algorithm for Real-Time Video Stream Analysis
We present 2SDS (Scene Separation and Data Selection algorithm), a temporal segmentation algorithm used in real-time video stream interpretation.
LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion
They are sent to the core of LXL, called "radar occupancy-assisted depth-based sampling", to aid image view transformation.
Set-Type Belief Propagation with Applications to Poisson Multi-Bernoulli SLAM
In this paper, we develop BP rules for factor graphs defined on sequences of RFSs where each RFS has an unknown number of elements, with the intention of deriving novel inference methods for RFSs.
Simultaneous Localization and Mapping
Vocal Bursts Type Prediction
Deep Fusion of Multi-Object Densities Using Transformer
We compare the performance of the transformer-based fusion method with a well-performing model-based Bayesian fusion method in several simulated scenarios with different parameter settings using synthetic data.
Trajectory PMB Filters for Extended Object Tracking Using Belief Propagation
First, we present a PMBM conjugate prior on the posterior of sets of trajectories for a generalized measurement model, in which each object generates an independent set of measurements.
A comparison between PMBM Bayesian track initiation and labelled RFS adaptive birth
This paper provides a comparative analysis between the adaptive birth model used in the labelled random finite set literature and the track initiation in the Poisson multi-Bernoulli mixture (PMBM) filter, with point-target models.
GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles
The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry.
Multiple Object Trajectory Estimation Using Backward Simulation
In this paper, we first derive a general multi-trajectory backward smoothing equation based on random finite sets of trajectories.
Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation
Deep learning-based instance segmentation enables real-time object identification from the radar detection points.
Can Deep Learning be Applied to Model-Based Multi-Object Tracking?
Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as autonomous driving, tracking animal behavior, defense systems, and others.
Deep Instance Segmentation with Automotive Radar Detection Points
Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points.
An Uncertainty-Aware Performance Measure for Multi-Object Tracking
Evaluating the performance of multi-object tracking (MOT) methods is not straightforward, and existing performance measures fail to consider all the available uncertainty information in the MOT context.
Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning
We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime.
A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets
This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter for coexisting point and extended targets, i. e., for scenarios where there may be simultaneous point and extended targets.
Backward Simulation for Sets of Trajectories
This paper presents a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories, from a sequence of multitarget (unlabelled) filtering densities and the multitarget dynamic model.
Trajectory Poisson multi-Bernoulli filters
The filters are based on propagating a Poisson multi-Bernoulli (PMB) density on the corresponding set of trajectories through the filtering recursion.
Poisson Multi-Bernoulli Mixtures for Sets of Trajectories
First, we show that, for the standard point target model, the PMBM density is conjugate also for sets of trajectories.
Multi-Scan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filter
A multi-scan trajectory PMBM filter and a multi-scan trajectory MBM filter, with the ability to correct past data association decisions to improve current decisions, are presented.
Signal Processing
Extended target Poisson multi-Bernoulli mixture trackers based on sets of trajectories
The Poisson multi-Bernoulli mixture (PMBM) is a multi-target distribution for which the prediction and update are closed.
Signal Processing
Gaussian implementation of the multi-Bernoulli mixture filter
This paper presents the Gaussian implementation of the multi-Bernoulli mixture (MBM) filter.
Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectories
By showing that the prediction and update in the PMBM filter can be viewed as an efficient method for calculating the time marginals of the RFS of trajectories, continuity in the same sense as MHT is established for the PMBM filter.
An Implementation of the Poisson Multi-Bernoulli Mixture Trajectory Filter via Dual Decomposition
This paper proposes an efficient implementation of the Poisson multi-Bernoulli mixture (PMBM) trajectory filter.
Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering
The Poisson multi-Bernoulli mixture (PMBM) is a multi-object conjugate prior for the closed-form Bayes random finite sets filter.
