7 citations · 8 across the 6 of their papers we have counts for
6 papers
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
Xingcheng Zhou, Konstantinos Larintzakis, Hao Guo +7
We present TUMTraffic-VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios. The dataset comprises 1,000 vid…
WARM-3D: A Weakly-Supervised Sim2Real Domain Adaptation Framework for Roadside Monocular 3D Object Detection
Xingcheng Zhou, Deyu Fu, Walter Zimmer +4
Existing roadside perception systems are limited by the absence of publicly available, large-scale, high-quality 3D datasets. Exploring the use of cost-effective, extensive synthet…
TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras
Christian Creß, Walter Zimmer, Nils Purschke +5
Event-based cameras are predestined for Intelligent Transportation Systems (ITS). They provide very high temporal resolution and dynamic range, which can eliminate motion blur and…
A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research
Christian Creß, Walter Zimmer, Leah Strand +4
Data-intensive machine learning based techniques increasingly play a prominent role in the development of future mobility solutions - from driver assistance and automation function…
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS
Rui Song, Liguo Zhou, Venkatnarayanan Lakshminarasimhan +2
Deep learning is a key approach for the environment perception function of Cooperative Intelligent Transportation Systems (C-ITS) with autonomous vehicles and smart traffic infrast…
Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation
Annkathrin Krämmer, Christoph Schöller, Dhiraj Gulati +5
The environmental perception of an autonomous vehicle is limited by its physical sensor ranges and algorithmic performance, as well as by occlusions that degrade its understanding…