No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles Using Cameras and LiDARs

Focal Loss for Dense Object Detection

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Vision meets robotics: The KITTI dataset

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Algorithms for the Assignment and Transportation Problems

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Tracking-Learning-Detection

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Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics

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Robust object tracking with online multiple instance learning

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A Boosted Particle Filter: Multitarget Detection and Tracking

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Multiple Object Tracking Using K-Shortest Paths Optimization

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Struck: Structured Output Tracking with Kernels

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Global data association for multi-object tracking using network flows

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Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

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Real time robust L1 tracker using accelerated proximal gradient approach

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Globally-optimal greedy algorithms for tracking a variable number of objects

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MCMC-based particle filtering for tracking a variable number of interacting targets

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Learning to Track: Online Multi-object Tracking by Decision Making

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Learning to associate: HybridBoosted multi-target tracker for crowded scene

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Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera

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Continuous Energy Minimization for Multitarget Tracking

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A multi-sensor fusion system for moving object detection and tracking in urban driving environments

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Robust Online Multi-object Tracking Based on Tracklet Confidence and Online Discriminative Appearance Learning

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Markov Chain Monte Carlo Data Association for Multi-Target Tracking

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Multi-target tracking by on-line learned discriminative appearance models

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Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow

Butt, A. A., & Collins, R. T. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.241 · Cited by 265 · see citing works

3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes

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Efficient representation of traffic scenes by means of dynamic stixels

Pfeiffer, D., & Franke, U. (2010) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5548114 · Cited by 131 · see citing works

Multi-Target Tracking using a 3D-Lidar sensor for autonomous vehicles

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Challenges of Ground Truth Evaluation of Multi-target Tracking

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Dynamic Probabilistic Drivability Maps for Lane Change and Merge Driver Assistance

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Detection and Tracking of Moving Objects Using 2.5D Motion Grids

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Vision-Based Multiple Interacting Targets Tracking via On-Line Supervised Learning

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3D object tracking using RGB and LIDAR data

Asvadi, A., Girão, P., Peixoto, P., & Nunes, U. (2016) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795718 · Cited by 77 · see citing works

Online Multi-target Tracking by Large Margin Structured Learning

Kim, S.-A., Kwak, S., Feyereisl, J., & Han, B. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37431-9_8 · Cited by 72 · see citing works

A full-3D voxel-based dynamic obstacle detection for urban scenario using stereo vision

Broggi, A., Cattani, S., Patander, M., Sabbatelli, M., & Zani, P. (2013) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2013.6728213 · Cited by 62 · see citing works

RefineNet: Refining Object Detectors for Autonomous Driving

Rajaram, R. N., Ohn-Bar, E., & Trivedi, M. (2016) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2695896 · Cited by 51 · see citing works

Stereovision-Based Multiple Object Tracking in Traffic Scenarios Using Free-Form Obstacle Delimiters and Particle Filters

Vatavu, A., Danescu, R. G., & Nedevschi, S. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2366248 · Cited by 48 · see citing works

Trajectories and Maneuvers of Surrounding Vehicles With Panoramic Camera Arrays

Dueholm, J., Kristoffersen, M. S., Satzoda, R., Moeslund, T., & Trivedi, M. (2016) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2016.2622921 · Cited by 42 · see citing works

Multi-scale object candidates for generic object tracking in street scenes

Ošep, A., Hermans, A., Engelmann, F., Klostermann, D., Mathias, M., & Leibe, B. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487487 · Cited by 39 · see citing works

Efficient extraction of human motion volumes by tracking

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Fast and Robust Object Detection Using Visual Subcategories

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Machine learning in tracking associations with stereo vision and lidar observations for an autonomous vehicle

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Object tracking with 3D LIDAR via multi-task sparse learning

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A Multimodal, Full-Surround Vehicular Testbed for Naturalistic Studies and Benchmarking: Design, Calibration and DeploymentPreprint

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Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road

Rangesh, A., & Trivedi, M. M. (2020) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2020.2966074 · Cited by 18 · see citing works

Vision-Based Front and Rear Surround Understanding Using Embedded Processors

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A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works

Robust Multi-Modality Multi-Object Tracking

Zhang, W., Zhou, H., Sun, S., Wang, Z., Shi, J., & Loy, C. C. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00245 · Cited by 250 · see citing works

Modeling Quality of IoT Experience in Autonomous Vehicles

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Real-Time 3D Object Detection and Classification in Autonomous Driving Environment Using 3D LiDAR and Camera Sensors

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Ditto Subsystem : A Case Study for Building Experiment Environment in StarBED Using TopDL

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A hovercraft testbed for decentralized and cooperative control

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Deterlab testbed for cybersecurity research and education

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A networked and cooperative control testbed

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Unmanned air vehicle testbed for cooperative control experiments

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