流媒体视频中目标检测的高性能架构

2013 23rd International Conference on Field programmable Logic and Applications Pub Date : 2013-10-24 DOI:10.1109/FPL.2013.6645559

P. Zemčík, Roman Juránek, Petr Musil, M. Musil, Michal Hradiš

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引用次数: 11

摘要

本文介绍了一种基于WaldBoost训练算法的高性能多尺度视频目标检测引擎的新架构。该架构的关键属性包括处理流数据和低资源消耗。我们在FPGA中实现了该引擎，并证明它可以在不需要外部存储器的情况下以超过160 fps的速度处理640×480像素视频流。我们评估了人脸检测任务的设计，将其与最先进的设计进行了比较，并讨论了其特点和局限性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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High performance architecture for object detection in streamed videos

In this paper, we introduce a novel architecture of an engine for high performance multi-scale detection of objects in videos based on WaldBoost training algorithm. The key properties of the architecture include processing of streamed data and low resource consumption. We implemented the engine in FPGA and we show that it can process 640×480 pixel video streams at over 160 fps without the need of external memory. We evaluate the design on the face detection task, compare it to state of the art designs, and discuss its features and limitations.

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2013 23rd International Conference on Field programmable Logic and Applications

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