A heuristic and reliable track-to-track data association approach for multi-cell track reconstruction

2021 ◽  
Author(s):  
Di Wu ◽  
Benlian Xu ◽  
Mingli Lu
Keyword(s):  
Data Association ◽  
Track Reconstruction ◽  
Cell Track

Data association method of SLAM based on ant colony algorithm

2009 ◽  
Vol 29 (1) ◽  
pp. 136-138 ◽  
Author(s):  
Wen-jing ZENG ◽  
Tie-dong ZHANG ◽  
Yu-ru XU ◽  
Da-peng JIANG
Keyword(s):  
Ant Colony Algorithm ◽  
Data Association ◽  
Ant Colony ◽  
Association Method

Data Association Algorithms for Tracking Satellites

2008 ◽  
Author(s):  
Lucy Y. Pao ◽  
Matthew Travers ◽  
Todd Murphey
Keyword(s):  
Data Association

scholarly journals Data association and uncertainty pruning for tracks determined on short arcs

2020 ◽  
Vol 132 (1) ◽  
Author(s):  
Laura Pirovano ◽  
Gennaro Principe ◽  
Roberto Armellin
Keyword(s):  
Optimization Problems ◽  
Data Association ◽  
Earth Orbit ◽  
Orbital State ◽  
Geostationary Earth Orbit ◽  
Multiple Observations ◽  
Observation Strategies

AbstractWhen building a space catalogue, it is necessary to acquire multiple observations of the same object for the estimated state to be considered meaningful. A first concern is then to establish whether different sets of observations belong to the same object, which is the association problem. Due to illumination constraints and adopted observation strategies, small objects may be detected on short arcs, which contain little information about the curvature of the orbit. Thus, a single detection is usually of little value in determining the orbital state due to the very large associated uncertainty. In this work, we propose a method that both recognizes associated observations and sequentially reduces the solution uncertainty when two or more sets of observations are associated. The six-dimensional (6D) association problem is addressed as a cascade of 2D and 4D optimization problems. The performance of the algorithm is assessed using objects in geostationary Earth orbit, with observations spread over short arcs.

scholarly journals A Two-Stage Data Association Approach for 3D Multi-Object Tracking

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2894
Author(s):  
Minh-Quan Dao ◽  
Vincent Frémont
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Moving Objects ◽  
Data Association ◽  
Autonomous Driving ◽  
Tracking Accuracy ◽  
Two Stage ◽  
Bipartite Matching ◽  
3D Object ◽  
3D Object Detection

Multi-Object Tracking (MOT) is an integral part of any autonomous driving pipelines because it produces trajectories of other moving objects in the scene and predicts their future motion. Thanks to the recent advances in 3D object detection enabled by deep learning, track-by-detection has become the dominant paradigm in 3D MOT. In this paradigm, a MOT system is essentially made of an object detector and a data association algorithm which establishes track-to-detection correspondence. While 3D object detection has been actively researched, association algorithms for 3D MOT has settled at bipartite matching formulated as a Linear Assignment Problem (LAP) and solved by the Hungarian algorithm. In this paper, we adapt a two-stage data association method which was successfully applied to image-based tracking to the 3D setting, thus providing an alternative for data association for 3D MOT. Our method outperforms the baseline using one-stage bipartite matching for data association by achieving 0.587 Average Multi-Object Tracking Accuracy (AMOTA) in NuScenes validation set and 0.365 AMOTA (at level 2) in Waymo test set.

scholarly journals A Compact Muon Tracker for Dynamic Tomography of Density Based on a Thin Time Projection Chamber with Micromegas Readout

Particles ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 333-342
Author(s):  
Ignacio Lázaro Roche
Keyword(s):  
Time Projection Chamber ◽  
Wide Field ◽  
Track Reconstruction ◽  
Long Term Stability ◽  
Societal Issues ◽  
Primary Signal ◽  
And Performance ◽  
Time Projection

Tomography based on cosmic muon absorption is a rising technique because of its versatility and its consolidation as a geophysics tool over the past decade. It allows us to address major societal issues such as long-term stability of natural and man-made large infrastructures or sustainable underwater management. Traditionally, muon trackers consist of hodoscopes or multilayer detectors. For applications with challenging available volumes or the wide field of view required, a thin time projection chamber (TPC) associated with a Micromegas readout plane can provide a good tradeoff between compactness and performance. This paper details the design of such a TPC aiming at maximizing primary signal and minimizing track reconstruction artifacts. The results of the measurements performed during a case study addressing the aforementioned applications are discussed. The current works lines and perspectives of the project are also presented.

scholarly journals Studying the Potential of Graphcore® IPUs for Applications in Particle Physics

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Samuel Maddrell-Mander ◽  
Lakshan Ram Madhan Mohan ◽  
Alexander Marshall ◽  
Daniel O’Hanlon ◽  
Konstantinos Petridis ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Kalman Filter ◽  
Particle Physics ◽  
Processing Unit ◽  
Network Architectures ◽  
Track Reconstruction ◽  
Event Simulation ◽  
Scattering Correction ◽  
New Type

AbstractThis paper presents the first study of Graphcore’s Intelligence Processing Unit (IPU) in the context of particle physics applications. The IPU is a new type of processor optimised for machine learning. Comparisons are made for neural-network-based event simulation, multiple-scattering correction, and flavour tagging, implemented on IPUs, GPUs and CPUs, using a variety of neural network architectures and hyperparameters. Additionally, a Kálmán filter for track reconstruction is implemented on IPUs and GPUs. The results indicate that IPUs hold considerable promise in addressing the rapidly increasing compute needs in particle physics.

Sign in / Sign up

Export Citation Format

Share Document