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Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving [article]

Hsu-kuang Chiu, Jie Li, Rares Ambrus, Jeannette Bohg
2021 arXiv   pre-print
Multi-object tracking is an important ability for an autonomous vehicle to safely navigate a traffic scene.  ...  We propose a probabilistic, multi-modal, multi-object tracking system consisting of different trainable modules to provide robust and data-driven tracking results.  ...  CONCLUSION In this paper, we propose an online probabilistic, multimodal, multi-object tracking algorithm for autonomous driving.  ... 
arXiv:2012.13755v2 fatcat:cqt5xumlmbgntffgdpcdpcidr4

Probabilistic 3D Multi-Object Tracking for Autonomous Driving [article]

Hsu-kuang Chiu, Antonio Prioletti, Jie Li, Jeannette Bohg
2020 arXiv   pre-print
3D multi-object tracking is a key module in autonomous driving applications that provides a reliable dynamic representation of the world to the planning module.  ...  Our experimental results on the NuScenes validation and test set show that our method outperforms the AB3DMOT baseline method by a large margin in the Average Multi-Object Tracking Accuracy (AMOTA) metric  ...  Introduction 3D multi-object tracking is essential for autonomous driving. Its aim is to estimate the location, orientation, and scale of all the objects in the environment over time.  ... 
arXiv:2001.05673v1 fatcat:2n6a5wrlm5bdjl3oniediaqwqa

Efficient Online Transfer Learning for 3D Object Classification in Autonomous Driving [article]

Rui Yang, Zhi Yan, Tao Yang, Yassine Ruichek
2021 arXiv   pre-print
This paper presents a multi-modal-based online learning system for 3D LiDAR-based object classification in urban environments, including cars, cyclists and pedestrians.  ...  Autonomous driving has achieved rapid development over the last few decades, including the machine perception as an important issue of it.  ...  Tixiao Shan for his initial barebone tracker package. UTBM is a member of the Autoware Foundation.  ... 
arXiv:2104.10037v3 fatcat:lqhgp2kdujbdvnb6s3dulsd42y

Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving [article]

Peixuan Li, Jieyu Jin
2022 arXiv   pre-print
While separately leveraging monocular 3D object detection and 2D multi-object tracking can be straightforwardly applied to sequence images in a frame-by-frame fashion, stand-alone tracker cuts off the  ...  transmission of the uncertainty from the 3D detector to tracking while cannot pass tracking error differentials back to the 3D detector.  ...  Introduction 3D object detection is an essential task for Autonomous Driving.  ... 
arXiv:2205.14882v1 fatcat:x574od67r5g4jnnppcs6hoijj4

SRCN3D: Sparse R-CNN 3D Surround-View Camera Object Detection and Tracking for Autonomous Driving [article]

Yining Shi, Jingyan Shen, Yifan Sun, Yunlong Wang, Jiaxin Li, Shiqi Sun, Kun Jiang, Diange Yang
2022 arXiv   pre-print
Detection And Tracking of Moving Objects (DATMO) is an essential component in environmental perception for autonomous driving.  ...  The proposal features and appearance features are both taken in data association process in a multi-hypotheses 3D multi-object tracking approach.  ...  Sparse R-CNN 3D Overview Problem Formulation. 3D object detection for autonomous driving perception system aims to classify the objects of interests into according categories and predict 3D bounding boxes  ... 
arXiv:2206.14451v1 fatcat:l7krlvijordd5fskv7kmttgurm

ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings [article]

Jiahui Huang, Sheng Yang, Tai-Jiang Mu, Shi-Min Hu
2020 arXiv   pre-print
scene understanding and autonomous driving.  ...  At the core of our system lies a multi-level probabilistic association mechanism and a heterogeneous Conditional Random Field (CRF) clustering approach combining semantic, spatial and motion information  ...  We thank anonymous reviewers for the valuable discussions.  ... 
arXiv:2003.12980v1 fatcat:qtp4osjtvbcpnhun7j5gjq2fbm

ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings

Jiahui Huang, Sheng Yang, Tai-Jiang Mu, Shi-Min Hu
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
scene understanding and autonomous driving.  ...  At the core of our system lies a multi-level probabilistic association mechanism and a heterogeneous Conditional Random Field (CRF) clustering approach combining semantic, spatial and motion information  ...  We thank anonymous reviewers for the valuable discussions.  ... 
doi:10.1109/cvpr42600.2020.00224 dblp:conf/cvpr/HuangYM020 fatcat:6n5augbfzjfzrp7y2qlxci62xq

Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies [article]

Yu Huang, Yue Chen
2020 arXiv   pre-print
This is a survey of autonomous driving technologies with deep learning methods.  ...  Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task  ...  SoCs and accelerators for autonomous driving vehicles.  ... 
arXiv:2006.06091v3 fatcat:nhdgivmtrzcarp463xzqvnxlwq

SDVTracker: Real-Time Multi-Sensor Association and Tracking for Self-Driving Vehicles [article]

Shivam Gautam, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Brian C. Becker
2020 arXiv   pre-print
Due to their computational efficiency, many traditional autonomy systems perform multi-object tracking using Kalman Filters which frequently rely on hand-engineered association.  ...  Accurate motion state estimation of Vulnerable Road Users (VRUs), is a critical requirement for autonomous vehicles that navigate in urban environments.  ...  We thank Blake Barber, Carl Wellington, Chengjie Zhang, David Wheeler, Gehua Yang, Kyle Ingersoll, Narek Melik-Barkhudarov, and Ralph Leyva for their support.  ... 
arXiv:2003.04447v1 fatcat:55rgimjf4rdibhid4ht56w2xru

Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art [article]

Joel Janai, Fatma Güney, Aseem Behl, Andreas Geiger
2021 arXiv   pre-print
, scene understanding, and end-to-end learning for autonomous driving.  ...  While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published.  ...  [707] extend [709] to a probabilistic 3D scene model that encompasses multi-class object detection, object tracking, scene labeling, and reasoning about geometric relations. Geiger et al.  ... 
arXiv:1704.05519v3 fatcat:xiintiarqjbfldheeg2hsydyra

MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review [article]

Zhiqing Wei, Fengkai Zhang, Shuo Chang, Yangyang Liu, Huici Wu, Zhiyong Feng
2022 arXiv   pre-print
In addition, we introduce three-dimensional(3D) object detection, the fusion of lidar and vision in autonomous driving and multimodal information fusion, which are promising for the future.  ...  First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving.  ...  However, 3D object detection currently is more attractive in the studies of autonomous driving.  ... 
arXiv:2108.03004v3 fatcat:xr5vch2xwbgb3gfnqp2b5cvqee

MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review

Zhiqing Wei, Fengkai Zhang, Shuo Chang, Yangyang Liu, Huici Wu, Zhiyong Feng
2022 Sensors  
In addition, we introduce three-dimensional(3D) object detection, the fusion of lidar and vision in autonomous driving and multimodal information fusion, which are promising for the future.  ...  First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving.  ...  However, 3D object detection currently is more attractive in the studies of autonomous driving.  ... 
doi:10.3390/s22072542 pmid:35408157 pmcid:PMC9003130 fatcat:ekeca2ul2fgb7jkurgwpt3fbh4

Pedestrian Models for Autonomous Driving Part I: low level models, from sensing to tracking [article]

Fanta Camara, Nicola Bellotto, Serhan Cosar, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, André Dietrich, Charles W. Fox
2020 arXiv   pre-print
Technologies at these levels are found to be mature and available as foundations for use in higher level systems such as behaviour modelling, prediction and interaction control.  ...  Autonomous vehicles (AVs) must share space with human pedestrians, both in on-road cases such as cars at pedestrian crossings and off-road cases such as delivery vehicles navigating through crowds on high-streets  ...  autonomous driving.  ... 
arXiv:2002.11669v1 fatcat:fgg5j5jdwrbujjgtj2uhgrx2am

Robust Stereo Visual Odometry Based on Probabilistic Decoupling Ego-Motion Estimation and 3D SSC

Yan Wang, Hui-qi Miao, Lei Guo
2019 IEEE Access  
The non-negative constraint makes the method suitable for fast moving camera. The proposed 3D-SSC method removes the outliers belonging to dynamic objects effectively.  ...  INDEX TERMS Stereo visual odometry, probabilistic matches, decoupling estimation, 3D SSC.  ...  C. 3D POINTS BASED SSC FOR HIGH SPEED The purpose of motion segmentation is to distinguish different motions between multi-trajectories of tracking points.  ... 
doi:10.1109/access.2018.2886824 fatcat:ucrph2egvjhtxawsyfwgmhkr44

Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras [article]

Zhengxia Zou, Rusheng Zhang, Shengyin Shen, Gaurav Pandey, Punarjay Chakravarty, Armin Parchami, Henry X. Liu
2022 arXiv   pre-print
The proposed framework covers a full-stack of roadside perception pipeline for infrastructure-assisted autonomous driving, including object detection, object localization, object tracking, and multi-camera  ...  We propose a novel and pragmatic framework for traffic scene perception with roadside cameras.  ...  As shown in Fig. 1 , the proposed scheme covers a full-stack of roadside perception pipeline for infrastructure-assisted autonomous drivingfrom object detection, localization, tracking, to multi-sensor  ... 
arXiv:2206.09770v1 fatcat:nr4tbimp4rgg5lntolkv2wxyda
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