most citedThe Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration

3 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Driver Activity Classification Using Generalizable Representations from Vision-Language Models

Ross Greer, Mathias Viborg Andersen, Andreas Møgelmose +1

Driver activity classification is crucial for ensuring road safety, with applications ranging from driver assistance systems to autonomous vehicle control transitions. In this pape…

cs.CV2024

OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilities

Lasse H. Hansen, Simon B. Jensen, Mark P. Philipsen +3

Identifying and classifying underground utilities is an important task for efficient and effective urban planning and infrastructure maintenance. We present OpenTrench3D, a novel a…

cs.CV20242 cited

Raw Instinct: Trust Your Classifiers and Skip the Conversion

Christos Kantas, Bjørk Antoniussen, Mathias V. Andersen +6

Using RAW-images in computer vision problems is surprisingly underexplored considering that converting from RAW to RGB does not introduce any new capture information. In this paper…

cs.CV2024

Learning to Find Missing Video Frames with Synthetic Data Augmentation: A General Framework and Application in Generating Thermal Images Using RGB Cameras

Mathias Viborg Andersen, Ross Greer, Andreas Møgelmose +1

Advanced Driver Assistance Systems (ADAS) in intelligent vehicles rely on accurate driver perception within the vehicle cabin, often leveraging a combination of sensing modalities.…

cs.CV20243 cited

The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration

Ross Greer, Bjørk Antoniussen, Mathias V. Andersen +2

Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data. We demons…