TEAMwISE: synchronised immersive environments for exploration and analysis of animal behaviour

Klein, Karsten; Aichem, Michael; Zhang, Ying; Erk, Stefan; Sommer, Björn; Schreiber, Falk

doi:10.1007/s12650-021-00746-2

Cited by 5 publications

(1 citation statement)

References 45 publications

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“…Both seek ways to create scalable and robust solutions [21,22] for faithful and reliable Fig. 1 Visual analysis in the TeamWise animal movement analysis tool [15], showing a behavior classification visualization along a movement trajectory on top of satellite imagery. The timeline bar at the bottom shows behavior categories along the time axis, and the behavior annotations are embedded in the 3D view.…”

Section: Introductionmentioning

confidence: 99%

Beyond the horizon: immersive developments for animal ecology research

Zhang

Klein

Schreiber

et al. 2023

Vis. Comput. Ind. Biomed. Art

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More diverse data on animal ecology are now available. This “data deluge” presents challenges for both biologists and computer scientists; however, it also creates opportunities to improve analysis and answer more holistic research questions. We aim to increase awareness of the current opportunity for interdisciplinary research between animal ecology researchers and computer scientists. Immersive analytics (IA) is an emerging research field in which investigations are performed into how immersive technologies, such as large display walls and virtual reality and augmented reality devices, can be used to improve data analysis, outcomes, and communication. These investigations have the potential to reduce the analysis effort and widen the range of questions that can be addressed. We propose that biologists and computer scientists combine their efforts to lay the foundation for IA in animal ecology research. We discuss the potential and the challenges and outline a path toward a structured approach. We imagine that a joint effort would combine the strengths and expertise of both communities, leading to a well-defined research agenda and design space, practical guidelines, robust and reusable software frameworks, reduced analysis effort, and better comparability of results.

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Section: Introductionmentioning

confidence: 99%

Beyond the horizon: immersive developments for animal ecology research

Zhang

Klein

Schreiber

et al. 2023

Vis. Comput. Ind. Biomed. Art

Self Cite

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ReLive: Bridging In-Situ and Ex-Situ Visual Analytics for Analyzing Mixed Reality User Studies

Hubenschmid

Wieland

Fink

et al. 2022

CHI Conference on Human Factors in Computing Systems

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Robust visualization of trajectory data

Zhang

Klein

Deußen

et al. 2022

It - Information Technology

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The analysis of movement trajectories plays a central role in many application areas, such as traffic management, sports analysis, and collective behavior research, where large and complex trajectory data sets are routinely collected these days. While automated analysis methods are available to extract characteristics of trajectories such as statistics on the geometry, movement patterns, and locations that might be associated with important events, human inspection is still required to interpret the results, derive parameters for the analysis, compare trajectories and patterns, and to further interpret the impact factors that influence trajectory shapes and their underlying movement processes. Every step in the acquisition and analysis pipeline might introduce artifacts or alterate trajectory features, which might bias the human interpretation or confound the automated analysis. Thus, visualization methods as well as the visualizations themselves need to take into account the corresponding factors in order to allow sound interpretation without adding or removing important trajectory features or putting a large strain on the analyst. In this paper, we provide an overview of the challenges arising in robust trajectory visualization tasks. We then discuss several methods that contribute to improved visualizations. In particular, we present practical algorithms for simplifying trajectory sets that take semantic and uncertainty information directly into account. Furthermore, we describe a complementary approach that allows to visualize the uncertainty along with the trajectories.

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TEAMwISE: synchronised immersive environments for exploration and analysis of animal behaviour

Cited by 5 publications

References 45 publications

Beyond the horizon: immersive developments for animal ecology research

Beyond the horizon: immersive developments for animal ecology research

ReLive: Bridging In-Situ and Ex-Situ Visual Analytics for Analyzing Mixed Reality User Studies

Robust visualization of trajectory data

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