arXiv subjects

László Kopácsi

Publications and source records attributed to László Kopácsi.

2 recordsLinked to original sources

arXiv2026

Heads Up!: Towards In Situ Photogrammetry Annotations and Augmented Reality Visualizations for Guided Backcountry Skiing

Backcountry skiing is an activity where a group of skiers navigate challenging environmental conditions to ski outside of managed areas. This activity requires careful monitoring and effective communication around the current weather and terrain conditions to ensure skier safety. We aim to support and facilitate this communication by providing backcountry guides with a set of in situ spatial annotation tools to communicate hazards and appropriate speeds to the ski recreationalists. A guide can use a tablet application to annotate a photogrammetry-based map of a mountainside, for example, one collected using a commercial camera drone, with hazard points, slow-down zones, and safe zones. These annotations are communicated to the skiers via visual overlays in augmented reality heads-up displays. We present a prototype consisting of a web application and a virtual reality display that mirror the guide's and skier's perspectives, enabling participatory interaction design studies in a safe environment.

cs.HC↗

arXiv2024

A Self-Supervised Method for Body Part Segmentation and Keypoint Detection of Rat Images

Recognition of individual components and keypoint detection supported by instance segmentation is crucial to analyze the behavior of agents on the scene. Such systems could be used for surveillance, self-driving cars, and also for medical research, where behavior analysis of laboratory animals is used to confirm the aftereffects of a given medicine. A method capable of solving the aforementioned tasks usually requires a large amount of high-quality hand-annotated data, which takes time and money to produce. In this paper, we propose a method that alleviates the need for manual labeling of laboratory rats. To do so, first, we generate initial annotations with a computer vision-based approach, then through extensive augmentation, we train a deep neural network on the generated data. The final system is capable of instance segmentation, keypoint detection, and body part segmentation even when the objects are heavily occluded.

cs.CV↗