论文标题
很少有来自无电池摄像机的极低质量图像的室内居住检测的射击聚类
Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free cameras
论文作者
论文摘要
可靠的室内环境中人类入住率的检测对于各种能源效率，安全性和安全应用至关重要。我们考虑使用低功率图像传感器的极低质量，隐私图像的占用检测挑战。我们提出了一个综合的射击学习和聚类算法，以应对这一挑战的挑战非常低。虽然少数拍摄的学习概念使我们能够通过一些标记的示例来委托系统，但聚类步骤是在线适应随着时间的推移改变成像环境的目的。除了在基准数据集上验证和比较我们的算法外，我们还使用新型的无电池相机硬件在从真实房屋中收集的流式图像上展示了我们的算法的性能。
Reliable detection of human occupancy in indoor environments is critical for various energy efficiency, security, and safety applications. We consider this challenge of occupancy detection using extremely low-quality, privacy-preserving images from low power image sensors. We propose a combined few shot learning and clustering algorithm to address this challenge that has very low commissioning and maintenance cost. While the few shot learning concept enables us to commission our system with a few labeled examples, the clustering step serves the purpose of online adaptation to changing imaging environment over time. Apart from validating and comparing our algorithm on benchmark datasets, we also demonstrate performance of our algorithm on streaming images collected from real homes using our novel battery free camera hardware.
