Crowd-AI Camera Sensing in the Real World
Crowd-AI Camera Sensing in the Real World
复制标题
现实世界中的群体人工智能摄像头感知
DOI：
10.1145/3264921
复制
发表时间：
2018
影响因子：
--
通讯作者：
Jeffrey P. Bigham
中科院分区：
文献类型：
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作者：
Anhong Guo;Anuraag Jain;Shomiron Ghose;Gierad Laput;Chris Harrison;Jeffrey P. Bigham
内置摄像头的智能设备，如Nest Cam和Amazon Echo Look，正在变得越来越普遍。它们有望将高保真度，丰富的上下文感知带入我们的家庭，工作场所和其他环境。尽管最近取得了令人印象深刻的进展，但计算机视觉系统仍然局限于它们可以回答的传感问题类型，更重要的是，它们不容易在不同的人类环境中推广。作为回应，研究人员研究了混合人群和人工智能驱动的方法，这些方法收集人类标签来引导自动化过程。然而，部署规模很小，而且大多局限于机构环境，这使得该方法的可扩展性和通用性成为悬而未决的问题。在这项工作中，我们描述了我们对Zensors++的迭代开发，这是一种基于摄像头的全栈人群AI传感系统，在规模、问题多样性、准确性、延迟和经济可行性方面大大超越了以前的工作。我们在野外部署了Zensors++，与真实的用户一起，在许多个月和环境中，为我们的参与者创建的近200个问题生成了160万个答案，每个答案的成本约为6/10美分。我们分享经验教训，收集的见解，以及对未来人群AI视觉系统的影响。
Smart appliances with built-in cameras, such as the Nest Cam and Amazon Echo Look, are becoming pervasive. They hold the promise of bringing high fidelity, contextually rich sensing into our homes, workplaces and other environments. Despite recent and impressive advances, computer vision systems are still limited in the types of sensing questions they can answer, and more importantly, do not easily generalize across diverse human environments. In response, researchers have investigated hybrid crowd- and AI-powered methods that collect human labels to bootstrap automatic processes. However, deployments have been small and mostly confined to institutional settings, leaving open questions about the scalability and generality of the approach. In this work, we describe our iterative development of Zensors++, a full-stack crowd-AI camera-based sensing system that moves significantly beyond prior work in terms of scale, question diversity, accuracy, latency, and economic feasibility. We deployed Zensors++ in the wild, with real users, over many months and environments, generating 1.6 million answers for nearly 200 questions created by our participants, costing roughly 6/10ths of a cent per answer delivered. We share lessons learned, insights gleaned, and implications for future crowd-AI vision systems.
DOI：
10.1109/cvpr.2018.00380
发表时间：
2018-02
期刊：
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
D. Gurari;Qing Li;Abigale Stangl;Anhong Guo;Chi Lin;K. Grauman;Jiebo Luo;Jeffrey P. Bigham
通讯作者：
D. Gurari;Qing Li;Abigale Stangl;Anhong Guo;Chi Lin;K. Grauman;Jiebo Luo;Jeffrey P. Bigham