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Hardware Implementation of a Smart Camera with Keypoint Detection and Description

机译:具有关键点检测和描述的智能相机的硬件实现

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Feature detection and description constitute important steps of many computer vision applications such as object detection and panorama stitching. Since those steps are computationally heavy, they might occupy significant portion of the full operation. Although fast feature detection algorithms and resource-efficient binary description methods have been proposed and implemented, resource limited embedded devices and distributed camera systems still require more effective solutions. In this paper, we propose a novel smart camera architecture which finds the FAST keypoints and computes their FREAK descriptions by processing pixel stream. Thus, this smart camera system provides useful metadata associated with the pixel stream at the same time with no latency. Moreover, performance of this hardware reaches very high frame rates with power and area efficiency. With this approach, this costly operation is locally solved in the smart camera node, and this leads to meet timing and power constraints of the large camera networks.
机译:特征检测和描述构成许多计算机视觉应用程序的重要步骤,例如对象检测和全景拼接。由于这些步骤在计算上很繁琐,因此它们可能会占用整个操作的很大一部分。尽管已经提出并实现了快速特征检测算法和资源高效的二进制描述方法,但是资源受限的嵌入式设备和分布式相机系统仍然需要更有效的解决方案。在本文中,我们提出了一种新颖的智能相机架构,该架构可以找到FAST关键点并通过处理像素流来计算其FREAK描述。因此,该智能相机系统没有延迟地同时提供了与像素流相关联的有用的元数据。此外,该硬件的性能在功率和面积效率方面达到了很高的帧速率。使用这种方法,可以在智能相机节点中本地解决此昂贵的操作,这将导致满足大型相机网络的时序和功率约束。

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