Dataset of Panoramic Images for People Tracking in Service Robotics

We provide a framework for constructing a guided robot for usage in hospitals in this thesis. The omnidirectional camera on the robot allows it to recognize and track the person who is following it. Furthermore, when directing the individual to their preferred position in the hospital, the robot must be aware of its surroundings and avoid accidents with other people or items. To train and evaluate our robot's performance, we developed an auto-labeling framework for creating a dataset of panoramic videos captured by the robot's omnidirectional camera. We labeled each person in the video and their real position in the robot's frame, enabling us to evaluate the accuracy of our tracking system and guide the development of the robot's navigation algorithms. Our research expands on earlier work that has established a framework for tracking individuals using omnidirectional cameras. We want to contribute to the continuing work to enhance the precision and dependability of these tracking systems, which is essential for the creation of efficient guiding robots in healthcare facilities, by developing a benchmark dataset. Our research has the potential to improve the patient experience and increase the efficiency of healthcare institutions by reducing staff time spent guiding patients through the facility.We provide a framework for constructing a guided robot for usage in hospitals in this thesis. The omnidirectional camera on the robot allows it to recognize and track the person who is following it. Furthermore, when directing the individual to their preferred position in the hospital, the robot must be aware of its surroundings and avoid accidents with other people or items. To train and evaluate our robot's performance, we developed an auto-labeling framework for creating a dataset of panoramic videos captured by the robot's omnidirectional camera. We labeled each person in the video and their real position in the robot's frame, enabling us to evaluate the accuracy of our tracking system and guide the development of the robot's navigation algorithms. Our research expands on earlier work that has established a framework for tracking individuals using omnidirectional cameras. We want to contribute to the continuing work to enhance the precision and dependability of these tracking systems, which is essential for the creation of efficient guiding robots in healthcare facilities, by developing a benchmark dataset. Our research has the potential to improve the patient experience and increase the efficiency of healthcare institutions by reducing staff time spent guiding patients through the facility

The Impact of Three Interfaces for 360-Degree Video on Spatial Cognition

In this paper, we describe an experiment designed to evaluate the effectiveness of three interfaces for surveillance or remote control using live 360-degree video feeds from a person or vehicle in the field. Video feeds are simulated using a game engine. While locating targets within a 3D terrain using a 2D 360-degree interface, participants indicated perceived egocentric directions to targets and later placed targets on an overhead view of the terrain. Interfaces were compared based on target finding and map placement performance. Results suggest 1) nonseamless interfaces with visual boundaries facilitate spatial understanding, 2) correct perception of self-to-object relationships is not correlated with understanding object-toobject relationships within the environment, and 3) increased video game experience corresponds with better spatial understanding of an environment observed in 360- degrees. This work can assist researchers of panoramic video systems in evaluating the optimal interface for observation and teleoperation of remote systems