Trajectory Poisson multi-Bernoulli filters

• URL: http://arxiv.org/abs/2003.12767v3
• Date: Thu, 17 Sep 2020 12:59:16 GMT
• Title: Trajectory Poisson multi-Bernoulli filters
• Authors: \'Angel F. Garc\'ia-Fern\'andez, Lennart Svensson, Jason L. Williams, Yuxuan Xia, Karl Granstr\"om
• Abstract summary: This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking. One to estimate the set of alive trajectories at each time step and another to estimate the set of all trajectories, which includes alive and dead trajectories, at each time step.
• Score: 5.545791216381868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to estimate the set of all trajectories, which includes alive and dead trajectories, at each time step. The filters are based on propagating a Poisson multi-Bernoulli (PMB) density on the corresponding set of trajectories through the filtering recursion. After the update step, the posterior is a PMB mixture (PMBM) so, in order to obtain a PMB density, a Kullback-Leibler divergence minimisation on an augmented space is performed. The developed filters are computationally lighter alternatives to the trajectory PMBM filters, which provide the closed-form recursion for sets of trajectories with Poisson birth model, and are shown to outperform previous multi-target tracking algorithms.

Related papers

• StraightPCF: Straight Point Cloud Filtering [50.66412286723848]
  Point cloud filtering is a fundamental 3D vision task, which aims to remove noise while recovering the underlying clean surfaces. We introduce StraightPCF, a new deep learning based method for point cloud filtering. It works by moving noisy points along straight paths, thus reducing discretization errors while ensuring faster convergence to the clean surfaces.
  arXiv  Detail & Related papers  (2024-05-14T05:41:59Z)
• Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring [71.60457491155451]
  Eliminating image blur produced by various kinds of motion has been a challenging problem. We propose a novel real-world deblurring filtering model called the Motion-adaptive Separable Collaborative Filter. Our method provides an effective solution for real-world motion blur removal and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-04-19T19:44:24Z)
• Closed-form Filtering for Non-linear Systems [83.91296397912218]
  We propose a new class of filters based on Gaussian PSD Models, which offer several advantages in terms of density approximation and computational efficiency. We show that filtering can be efficiently performed in closed form when transitions and observations are Gaussian PSD Models. Our proposed estimator enjoys strong theoretical guarantees, with estimation error that depends on the quality of the approximation and is adaptive to the regularity of the transition probabilities.
  arXiv  Detail & Related papers  (2024-02-15T08:51:49Z)
• Thompson sampling for improved exploration in GFlowNets [75.89693358516944]
  Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over compositional objects as a sequential decision-making problem with a learnable action policy. We show in two domains that TS-GFN yields improved exploration and thus faster convergence to the target distribution than the off-policy exploration strategies used in past work.
  arXiv  Detail & Related papers  (2023-06-30T14:19:44Z)
• A Moving Window Based Approach to Multi-scan Multi-Target Tracking [0.0]
  Multi-target state estimation refers to estimating the number of targets and their trajectories in a surveillance area. We propose a moving window based solution for multi-target tracking using the Generalized Labeled Multi-Bernoulli (GLMB) smoother.
  arXiv  Detail & Related papers  (2022-10-08T12:37:27Z)
• Interaction-Aware Labeled Multi-Bernoulli Filter [5.255783459833821]
  We present a novel approach to incorporate target interactions within the prediction step of an RFS-based multi-target filter. The method has been developed for two practical applications of tracking a coordinated swarm and vehicles.
  arXiv  Detail & Related papers  (2022-04-19T04:23:32Z)
• Extended Object Tracking Using Sets Of Trajectories with a PHD Filter [6.0813324895213885]
  PHD filtering is a common and effective multiple object tracking algorithm used in scenarios where the number of objects and their states are unknown. This paper presents a Gamma Gaussian inverse Wishart mixture PHD filter that can directly estimate sets of trajectories of extended targets.
  arXiv  Detail & Related papers  (2021-09-02T15:32:12Z)
• Continuous-discrete multiple target tracking with out-of-sequence measurements [0.25782420501870296]
  This paper derives the optimal Bayesian processing of an out-of-sequence (OOS) set of measurements in continuous-time for multiple target tracking. We consider a multi-target system modelled in continuous time that is discretised at the time steps when we receive the measurements.
  arXiv  Detail & Related papers  (2021-06-09T08:37:01Z)
• A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets [5.949779668853555]
  This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter for coexisting point and extended targets. As a computationally efficient approximation of the PMBM filter, we also develop a Poisson multi-Bernoulli (PMB) filter for coexisting point and extended targets.
  arXiv  Detail & Related papers  (2020-11-09T14:41:40Z)
• Deep Shells: Unsupervised Shape Correspondence with Optimal Transport [52.646396621449]
  We propose a novel unsupervised learning approach to 3D shape correspondence. We show that the proposed method significantly improves over the state-of-the-art on multiple datasets.
  arXiv  Detail & Related papers  (2020-10-28T22:24:07Z)
• Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets [96.98888948518815]
  State-of-the-art multi-object tracking(MOT) methods follow the tracking-by-detection paradigm. We propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes.
  arXiv  Detail & Related papers  (2020-07-18T19:51:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.