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FANTrack: 3D Multi-Object Tracking with Feature Association Network
[article]
2019
arXiv
pre-print
Preserved Fulltext
We propose a data-driven approach to online multi-object tracking (MOT) that uses a convolutional neural network (CNN) for data association in a tracking-by-detection framework. ...
The problem of multi-target tracking aims to assign noisy detections to a-priori unknown and time-varying number of tracked objects across a sequence of frames. ...
CONCLUSIONS In this paper, we presented a solution to the problem of data association in 3D online multi-object tracking using deep learning with multi-modal data. ...
arXiv:1905.02843v1
fatcat:scu6u47cxreuxiqigsllwhruga
Graph Neural Networks for 3D Multi-Object Tracking
[article]
2020
arXiv
pre-print
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3D Multi-object tracking (MOT) is crucial to autonomous systems. ...
Recent work often uses a tracking-by-detection pipeline, where the feature of each object is extracted independently to compute an affinity matrix. ...
Approach The goal of online MOT is to associate existing tracked objects from previous frame with detected objects in the current frame. ...
arXiv:2008.09506v1
fatcat:hvskir4smzdotclylcc7iosefi
Robust 3D Detection in Traffic Scenario with Tracking-Based Coupling System
[chapter]
2020
IFIP Advances in Information and Communication Technology
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In this paper, we propose a coupling system which combines 3D object detection and multi-object tracking into one framework. ...
MOT (Multi-Object Tracking) performance is heavily dependent on object detection. ...
In the MOT part, detection objects are associate with tracked objects. ...
doi:10.1007/978-3-030-49161-1_28
fatcat:in7pp3ttsjesth4pwuyalmb3yi
Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds
[article]
2021
arXiv
pre-print
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Based on the spatio-temporal features generated by the detector, the tracker associates the detected objects with previously tracked objects using a graph neural network (GNN). ...
In this paper, we propose a new joint object detection and tracking (JoDT) framework for 3D object detection and tracking based on camera and LiDAR sensors. ...
Then, it associates the detected objects with those in the tracklet through a graph neural network (GNN). ...
arXiv:2112.07116v2
fatcat:xzl6xzf2pbcu3buab5fpnhfmne
Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds
2022
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
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The file type is application/pdf.
Based on the spatio-temporal features generated by the detector, the tracker associates the detected objects with previously tracked objects using a graph neural network (GNN). ...
In this paper, we propose a new joint object detection and tracking (JoDT) framework for 3D object detection and tracking based on camera and LiDAR sensors. ...
Then, it associates the detected objects with those in the tracklet through a graph neural network (GNN). ...
doi:10.1609/aaai.v36i1.20007
fatcat:x5xf53hikvdbbi6dljaoeglr7m
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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3D Multi-object tracking (MOT) is crucial to autonomous systems. ...
As a result, the feature of one object is informed of the features of other objects so that the object feature can lean towards the object with similar feature (i.e., object probably with a same ID) and ...
Related Work Online Multi-Object Tracking. ...
doi:10.1109/cvpr42600.2020.00653
dblp:conf/cvpr/WengWMK20
fatcat:etvfmtkccfce3iixim567utuyu
3D Multi-Object Tracking: A Baseline and New Evaluation Metrics
[article]
2020
arXiv
pre-print
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3D multi-object tracking (MOT) is an essential component for many applications such as autonomous driving and assistive robotics. ...
Therefore, we propose a new 3D MOT evaluation tool along with three new metrics to comprehensively evaluate 3D MOT methods. ...
RELATED WORK 2D Multi-Object Tracking. Recent 2D MOT systems can be split into batch and online methods based on data association. ...
arXiv:1907.03961v5
fatcat:zzqj5xizybevjbhxamojpetcey
End-to-End 3D Multi-Object Tracking and Trajectory Forecasting
[article]
2020
arXiv
pre-print
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3D multi-object tracking (MOT) and trajectory forecasting are two critical components in modern 3D perception systems. ...
First, we employ a feature interaction technique by introducing Graph Neural Networks (GNNs) to capture the way in which multiple agents interact with one another. ...
Our method, which (1) is jointly trained with a 3D MOT head, (2) uses GNNs for feature interaction and (3) uses diversity sampling, outperforms the baselines in both accuracy and diversity metrics. ...
arXiv:2008.11598v1
fatcat:qqemnngoaffzrkqhsjwdtcetza
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning
[article]
2020
arXiv
pre-print
Preserved Fulltext
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The file type is application/pdf.
3D Multi-object tracking (MOT) is crucial to autonomous systems. ...
As a result, the feature of one object is informed of the features of other objects so that the object feature can lean towards the object with similar feature (i.e., object probably with a same ID) and ...
Related Work Online Multi-Object Tracking. ...
arXiv:2006.07327v1
fatcat:ow4rkn2hcbejfp5bycpgoj5li4
EagerMOT: 3D Multi-Object Tracking via Sensor Fusion
[article]
2021
arXiv
pre-print
Preserved Fulltext
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Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time. ...
., LiDAR) to detect and track targets in 3D space, but only up to a limited sensing range due to the sparsity of the signal. ...
Fantrack: 3d multi-object tracking with feature association network. [31] W. Shi and R. R. Rajkumar. Point-gnn: Graph neural network for 3d
In Intel. ...
arXiv:2104.14682v1
fatcat:nl2jfabqynhjfgg6qtryjyld2q
FlowMOT: 3D Multi-Object Tracking by Scene Flow Association
[article]
2021
arXiv
pre-print
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Most end-to-end Multi-Object Tracking (MOT) methods face the problems of low accuracy and poor generalization ability. ...
Then we use Hungarian algorithm to generate optimal matching relations with the ID propagation strategy to finish the tracking task. ...
[10] proposed to interact with appearance features through a Graph Neural Network, thus helping the association and tracking process. ...
arXiv:2012.07541v3
fatcat:idhlug2unnc6xb7hhcv2oqbxf4
Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation
[article]
2020
arXiv
pre-print
Preserved Fulltext
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The file type is application/pdf.
Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning. As a result, 3D multi-object tracking (MOT) plays a vital role in autonomous navigation. ...
Most MOT methods use a tracking-by-detection pipeline, which includes object detection and data association processing. ...
Some models [31, 46] build a neural network with a min-cost flow algorithm to optimize the total cost for the data association problem.
3D Multi-Object Tracking 3D object detection has made a great ...
arXiv:2011.12850v1
fatcat:ewh3c2arajegdgxobvlspcfskq
Visual-LiDAR based 3D Object Detection and Tracking for Embedded Systems
2020
IEEE Access
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The tracking information is merged with visual detections to provide 3D object poses along with associated tracking parameters.
IV. ...
The metrics used to evaluate the proposed MODT are: (a) tracker to target assignment, (b) multi object tracking accuracy MOTA, (c) multi object tracking precision MOTP, and (d) track quality. ...
doi:10.1109/access.2020.3019187
fatcat:2macumn6fzhkjmylsvxs5tk7p4
Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation
2021
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning. As a result, 3D multi-object tracking (MOT) plays a vital role in autonomous navigation. ...
Most MOT methods use a tracking-by-detection pipeline, which includes both the object detection and data association tasks. ...
Some models [26, 27] build a neural network with a min-cost flow algorithm to optimize the total cost for the data association problem.
3D Multi-Object Tracking 3D object detection has achieved great ...
doi:10.3390/s21062113
pmid:33803021
pmcid:PMC8002739
fatcat:a3tmunoienfj3ldp63hj5wxxxy
A 3D Multiobject Tracking Algorithm of Point Cloud Based on Deep Learning
2020
Mathematical Problems in Engineering
Preserved Fulltext
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Given the good performance of the 2D MOT, this paper proposes a 3D MOT algorithm with deep learning based on the multiobject tracking algorithm. ...
Firstly, a 3D object detector was used to obtain oriented 3D bounding boxes from point clouds. ...
Figure 1 : 1 Our proposed 3D MOT system is composed by 3D object detection and tracking (data association and filtering) components. ...
doi:10.1155/2020/8895696
fatcat:fbq24uaek5gehihsopkhjysoou
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