CollabAR: Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality
CollabAR: Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality
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DOI：
10.1109/ipsn48710.2020.00-26
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Zida Liu;Guohao Lan;Jovan Stojkovic;Yunfan Zhang;Carlee Joe-Wong;M. Gorlatova
中科院分区：
文献类型：
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作者：
Zida Liu;Guohao Lan;Jovan Stojkovic;Yunfan Zhang;Carlee Joe-Wong;M. Gorlatova
移动增强现实（AR）在用户周围的现实场景上覆盖了数字内容，它带来了沉浸式的互动体验，在这些体验中，真实和虚拟世界紧密地耦合到可以启用无缝和精确的AR体验，这是一种可以启用的图像识别系统。由于图像扭曲的普遍性和严重性，需要准确地识别具有低系统延迟的相机视图。 AR仍然难以捉摸使失真自适应的图像识别能够提高对图像失真的鲁棒性，而后者则利用移动AR用户之间的“时空”相关性来改善识别准确性。商品移动设备的潜伏期低至17.8ms。
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 mobile AR is still elusive. In this paper, 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. We implement CollabAR on four different commodity devices, and evaluate its performance on two multi-view image datasets. Our evaluation demonstrates that CollabAR achieves over 96% recognition accuracy for images with severe distortions, while reducing the end-to-end system latency to as low as 17.8ms for commodity mobile devices.