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Mobile Augmented Reality (AR), which overlays digital information with real-world scenes surrounding a user, provides an enhanced mode of interaction with the ambient world. Contextual AR applications rely on image recognition to identify objects in the view of the mobile device. In practice, due to image distortions and device resource constraints, achieving high performance image recognition for AR is challenging. Recent advances in edge computing offer opportunities for designing collaborative image recognition frameworks for AR. In this demonstration, we present CollabAR, an edge-assisted collaborative image recognition framework. CollabAR allows AR devices that are facing the same scene to collaborate on the recognition task. Demo participants develop an intuition for different image distortions and their impact on image recognition accuracy. We showcase how heterogeneous images taken by different users can be aggregated to improve recognition accuracy and provide a better user experience in AR.
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The Effects of Network Outages on User Experience in Augmented Reality Based Remote Collaboration - An Empirical Study
Augmented Reality (AR) applications can enable geographically distant users to collaborate using shared video feeds or interactive 3D holograms, and may be particularly useful in the socially distant context of the Covid-19 pandemic. However, a good user experience is key for their success and could be negatively impacted by network impairments, which are an inevitable occurrence in today's best-effort Internet. In this paper, we present the findings of an empirical user study, aimed at understanding the effects of network outages, on user experience and behavior, in a collaborative AR task. We highlight how network outages affected users in different ways depending on their role in the collaborative task, and how giving users explicit information about poor network conditions helped them deal with some of these negative effects. Furthermore, we report the strategies that users themselves adopted, to deal with outages, such as batching instructions, or shifting to a different spatial referencing style when communicating with their partners. Lastly, based on our findings, we present some design implications for future remote-collaborative AR applications.
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- Award ID(s):
- 1815046
- PAR ID:
- 10603257
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 5
- Issue:
- CSCW2
- ISSN:
- 2573-0142
- Format(s):
- Medium: X Size: p. 1-27
- Size(s):
- p. 1-27
- Sponsoring Org:
- National Science Foundation
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