6 papers
Affordance-Guided Coarse-to-Fine Exploration for Base Placement in Open-Vocabulary Mobile Manipulation
Tzu-Jung Lin, Jia-Fong Yeh, Hung-Ting Su +3
In open-vocabulary mobile manipulation (OVMM), task success often hinges on the selection of an appropriate base placement for the robot. Existing approaches typically navigate to…
Improving Generalization Ability for 3D Object Detection by Learning Sparsity-invariant Features
Hsin-Cheng Lu, Chung-Yi Lin, Winston H. Hsu
In autonomous driving, 3D object detection is essential for accurately identifying and tracking objects. Despite the continuous development of various technologies for this task, a…
Enhancing Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approach
ChungYi Lin, Shen-Lung Tung, Hung-Ting Su +1
Traditional traffic prediction, limited by the scope of sensor data, falls short in comprehensive traffic management. Mobile networks offer a promising alternative using network ac…
Tel2Veh: Fusion of Telecom Data and Vehicle Flow to Predict Camera-Free Traffic via a Spatio-Temporal Framework
ChungYi Lin, Shen-Lung Tung, Hung-Ting Su +1
Vehicle flow, a crucial indicator for transportation, is often limited by detector coverage. With the advent of extensive mobile network coverage, we can leverage mobile user activ…
Tracking-Assisted Object Detection with Event Cameras
Ting-Kang Yen, Igor Morawski, Shusil Dangi +5
Event-based object detection has recently garnered attention in the computer vision community due to the exceptional properties of event cameras, such as high dynamic range and no…
TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling
ChungYi Lin, Shen-Lung Tung, Hung-Ting Su +1
To address the limitations of traffic prediction from location-bound detectors, we present Geographical Cellular Traffic (GCT) flow, a novel data source that leverages the extensiv…