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Related Experiment Video

Updated: Sep 26, 2025

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Monocular Quasi-Dense 3D Object Tracking.

Hou-Ning Hu, Yung-Hsu Yang, Tobias Fischer

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 19, 2022

Summary

This summary is machine-generated.

This study introduces a novel 3D object tracking framework using only camera images for autonomous driving. It achieves significant accuracy improvements in 3D tracking, outperforming previous vision-only methods.

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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

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Published on: July 21, 2020

Area of Science:

Computer Vision
Robotics
Autonomous Systems

Background:

Accurate 3D object tracking is crucial for autonomous driving and robotics.
Existing methods often rely on multiple sensors, limiting their applicability.

Purpose of the Study:

To develop a robust 3D object tracking framework using only 2D images.
To improve the association and prediction of moving objects in complex urban environments.

Main Methods:

Quasi-dense similarity learning for object association using appearance cues.
3D bounding box depth-ordering heuristics for robust instance association.
LSTM-based module for aggregating long-term trajectory and predicting velocity.

Main Results:

The framework demonstrates robust object association and tracking in urban scenarios across KITTI, nuScenes, and Waymo datasets.
Established the first camera-only baseline for 3D tracking and detection on the Waymo Open benchmark.
Achieved a nearly fivefold increase in tracking accuracy on the nuScenes 3D tracking benchmark compared to prior vision-only methods.

Conclusions:

The proposed framework offers a reliable and accurate vision-only solution for 3D object tracking.
It significantly advances the state-of-the-art in camera-based autonomous driving perception systems.