Computer Vision with a Superpixelation Camera
For computer vision engineers deploying models on edge devices, SuperCam offers a hardware-software solution to reduce data bandwidth and memory usage while maintaining performance.
The paper proposes SuperCam, a camera that performs superpixel segmentation on the fly to reduce data redundancy, and shows it outperforms state-of-the-art superpixel algorithms in memory-constrained settings, improving image segmentation, object detection, and monocular depth estimation.
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.