Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking

• URL: http://arxiv.org/abs/2004.10798v1
• Date: Wed, 22 Apr 2020 19:07:35 GMT
• Title: Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking
• Authors: Bahman Moraffah and Antonia Papndreou-Suppopola
• Abstract summary: In this paper, we employ Bayesian nonparametric methods to address challenges in tracking multiple objects. We propose modeling the multiple object parameter state prior using the dependent Dirichlet and Pitman-Yor processes. These nonparametric models have been shown to be more flexible and robust, when compared to existing methods.
• Score: 1.8275108630751837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Some challenging problems in tracking multiple objects include the time-dependent cardinality, unordered measurements and object parameter labeling. In this paper, we employ Bayesian Bayesian nonparametric methods to address these challenges. In particular, we propose modeling the multiple object parameter state prior using the dependent Dirichlet and Pitman-Yor processes. These nonparametric models have been shown to be more flexible and robust, when compared to existing methods, for estimating the time-varying number of objects, providing object labeling and identifying measurement to object associations. Monte Carlo sampling methods are then proposed to efficiently learn the trajectory of objects from noisy measurements. Using simulations, we demonstrate the estimation performance advantage of the new methods when compared to existing algorithms such as the generalized labeled multi-Bernoulli filter.

Related papers

• Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing [20.99082981430798]
  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. Deep learning based algorithms are a possible venue for tackling this issue but have not been applied extensively in settings where accurate multi-object models are available. We propose a novel DL architecture specifically tailored for this setting that decouples the data association task from the smoothing task.
  arXiv  Detail & Related papers  (2023-12-22T20:24:39Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• Object-Centric Conformance Alignments with Synchronization (Extended Version) [57.76661079749309]
  We present a new formalism that combines the ability of object-centric Petri nets to capture one-to-many relations and the one of Petri nets with identifiers to compare and synchronize objects based on their identity. We propose a conformance checking approach for such nets based on an encoding in satisfiability modulo theories (SMT)
  arXiv  Detail & Related papers  (2023-12-13T21:53:32Z)
• Three-dimensional Tracking of a Large Number of High Dynamic Objects from Multiple Views using Current Statistical Model [0.0]
  Three-dimensional tracking of multiple objects from multiple views has a wide range of applications. Current statistical model based Kalman particle filter (CSKPF) method is proposed following the Bayesian tracking-while-reconstruction framework. simulation experiments prove that the CSKPF method can improve the tracking integrity, continuity, and precision compared with the existing constant velocity based particle filter (CVPF) method.
  arXiv  Detail & Related papers  (2023-09-26T10:36:59Z)
• S$^3$Track: Self-supervised Tracking with Soft Assignment Flow [45.77333923477176]
  We study self-supervised multiple object tracking without using any video-level association labels. We propose differentiable soft object assignment for object association. We evaluate our proposed model on the KITTI, nuScenes, and Argoverse datasets.
  arXiv  Detail & Related papers  (2023-05-17T06:25:40Z)
• Dynamic Proposals for Efficient Object Detection [48.66093789652899]
  We propose a simple yet effective method which is adaptive to different computational resources by generating dynamic proposals for object detection. Our method achieves significant speed-up across a wide range of detection models including two-stage and query-based models.
  arXiv  Detail & Related papers  (2022-07-12T01:32:50Z)
• Can Deep Learning be Applied to Model-Based Multi-Object Tracking? [25.464269324261636]
  Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements. Deep learning (DL) has been increasingly used in MOT for improving tracking performance. In this paper, we propose a Transformer-based DL tracker and evaluate its performance in the model-based setting.
  arXiv  Detail & Related papers  (2022-02-16T07:43:08Z)
• A Few-Shot Sequential Approach for Object Counting [63.82757025821265]
  We introduce a class attention mechanism that sequentially attends to objects in the image and extracts their relevant features. The proposed technique is trained on point-level annotations and uses a novel loss function that disentangles class-dependent and class-agnostic aspects of the model. We present our results on a variety of object-counting/detection datasets, including FSOD and MS COCO.
  arXiv  Detail & Related papers  (2020-07-03T18:23:39Z)
• Evaluating the Disentanglement of Deep Generative Models through Manifold Topology [66.06153115971732]
  We present a method for quantifying disentanglement that only uses the generative model. We empirically evaluate several state-of-the-art models across multiple datasets.
  arXiv  Detail & Related papers  (2020-06-05T20:54:11Z)
• CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus [62.86856923633923]
  We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-08T17:37:01Z)

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.