Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality
Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality
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DOI：
10.1145/3469033
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发表时间：
2022-02
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影响因子：
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通讯作者：
Guohao Lan;Zida Liu;Yunfan Zhang;T. Scargill;Jovan Stojkovic;Carlee Joe-Wong;M. Gorlatova
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文献类型：
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作者：
Guohao Lan;Zida Liu;Yunfan Zhang;T. Scargill;Jovan Stojkovic;Carlee Joe-Wong;M. Gorlatova
移动的增强现实（AR）将数字内容覆盖在用户周围的真实世界场景上，正在带来沉浸式交互体验，其中真实的和虚拟世界紧密耦合。为了实现无缝和精确的AR体验，需要能够以低系统延迟准确识别相机视图中的对象的图像识别系统。然而，由于图像失真的普遍性和严重性，用于“野外”移动的AR的有效且鲁棒的图像识别解决方案仍然是难以捉摸的。在这篇文章中，我们提出了CollabAR，边缘辅助系统，提供失真容忍图像识别的移动的AR与不可察觉的系统延迟。CollabAR在其设计中结合了失真容忍和协作图像识别模块。前者使失真自适应图像识别，以提高对图像失真的鲁棒性，而后者利用移动的AR用户之间的时空相关性，以提高识别精度。此外，由于很难收集大规模的图像失真数据集，我们提出了一种基于循环一致生成对抗网络的数据增强方法来合成真实的图像失真。我们的评估表明，CollabAR对严重失真的“野外”图像的识别准确率超过85%，同时将端到端系统延迟降低到18.2 ms。
Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR with imperceptible system latency . CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for “in the wild” images with severe distortions, while reducing the end-to-end system latency to as low as 18.2 ms.