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FANTrack: 3D Multi-Object Tracking with Feature Association Network [article]

Erkan Baser, Venkateshwaran Balasubramanian, Prarthana Bhattacharyya, Krzysztof Czarnecki
2019 arXiv   pre-print
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]

Xinshuo Weng, Yongxin Wang, Yunze Man, Kris Kitani
2020 arXiv   pre-print
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]

Zhuoli Zhou, Shitao Chen, Rongyao Huang, Nanning Zheng
2020 IFIP Advances in Information and Communication Technology  
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]

Junho Koh, Jaekyum Kim, Jinhyuk Yoo, Yecheol Kim, Dongsuk Kum, Jun Won Choi
2021 arXiv   pre-print
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

Junho Koh, Jaekyum Kim, Jin Hyeok Yoo, Yecheol Kim, Dongsuk Kum, Jun Won Choi
2022 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
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

Xinshuo Weng, Yongxin Wang, Yunze Man, Kris M. Kitani
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
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]

Xinshuo Weng and Jianren Wang and David Held and Kris Kitani
2020 arXiv   pre-print
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]

Xinshuo Weng, Ye Yuan, Kris Kitani
2020 arXiv   pre-print
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]

Xinshuo Weng, Yongxin Wang, Yunze Man, Kris Kitani
2020 arXiv   pre-print
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]

Aleksandr Kim, Aljoša Ošep, Laura Leal-Taixé
2021 arXiv   pre-print
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]

Guangyao Zhai, Xin Kong, Jinhao Cui, Yong Liu, Zhen Yang
2021 arXiv   pre-print
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]

Can Chen, Luca Zanotti Fragonara, Antonios Tsourdos
2020 arXiv   pre-print
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

Muhammad Sualeh, Gon-Woo Kim
2020 IEEE Access  
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

Can Chen, Luca Zanotti Zanotti Fragonara, Antonios Tsourdos
2021 Sensors  
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

Dengjiang Wang, Chao Huang, Yajun Wang, Yongqiang Deng, Hongqiang Li, Bekir Sahin
2020 Mathematical Problems in Engineering  
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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