Privacy Aware Human-Object Interaction in the Wild Novel Dataset

Luna Lux Fredenslund, Vasiliki Ismiroglou, Thomas B. Moeslund, Kamal Nasrollahi

Abstract: Monitoring people in the wild is important for safety surveillance cameras enabling faster emergency responses, autonomous vehicles, and mobile robotic platforms. However, it also raises concerns around privacy and mass surveillance. To this end, thermal imagery is often applied as it reduces the privacy concerns, and works well in a variety of different weather and lighting conditions. Unfortunately, fewer thermal datasets are available compared to RGB datasets. In particular, there is a need for thermal datasets for human-object interaction detection which is the frontier for human scene understanding required for enabling the safety and autonomous tasks. Therefore, to advance the research field within real-world HOI scenarios, we introduce the first real-world thermal HOI dataset. The dataset uses surveillance video of a harbor (Nikolow et al. 2021 [17]), and captures the long tail of real-world behavior presenting a challenge for machine learning systems.

External IDs:dblp:conf/acivs/FredenslundIMN25