超像素相机实时分割图像，减少数据冗余，提升边缘设备视觉性能。
Computer Vision with a Superpixelation Camera
- 相机内置实时超像素分割，动态压缩图像数据。
- 内存受限时，超像素数据在图像分割等任务上表现更优。
- 适合部署于资源受限的边缘视觉系统，如智能摄像头。
传统相机生成的数据量庞大，对资源受限的应用处理困难。通常，相机输出的数据流规模与图像像素数相当，但多数数据对下游计算机视觉算法而言是冗余的。本文提出一种新型相机设计——SuperCam，通过现场执行超像素分割，自适应地处理捕获数据。实验表明，在内存受限条件下，SuperCam优于当前最先进的超像素算法。我们还评估了压缩数据在图像分割、目标检测和单目深度估计等下游任务中的表现，结果证明该设计在有限内存环境下能提供更优输出。我们认为，随着更多视觉模型部署于边缘设备，超像素分割将发挥关键作用，SuperCam有助于工程师构建更高效的视觉系统。
原文摘要 · Abstract (English)
Conventional cameras generate a lot of data that can be challenging to process in resource-constrained applications. Usually, cameras generate data streams on the order of the number of pixels in the image. However, most of this captured data is redundant for many downstream computer vision algorithms. We propose a novel camera design, which we call SuperCam, that adaptively processes captured data by performing superpixel segmentation on the fly. We show that SuperCam performs better than current state-of-the-art superpixel algorithms under memory-constrained situations. We also compare how well SuperCam performs when the compressed data is used for downstream computer vision tasks. Our results demonstrate that the proposed design provides superior output for image segmentation, object detection, and monocular depth estimation in situations where the available memory on the camera is limited. We posit that superpixel segmentation will play a crucial role as more computer vision inference models are deployed in edge devices. SuperCam would allow computer vision engineers to design more efficient systems for these applications.
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