Human action recognition (HAR) is an important field of research that intercepts with areas such as image processing, computer vision, and the design of fast algorithms, among others. HAR has several important applications including healthcare monitoring, security and surveillance, assisted living, smart homes, and video search and indexing. Despite recent developments in the field, major challenges remain. For instance, HAR is computationally expensive. Tasks such as video preprocessing, feature extraction, feature quantization, and feature classification require the execution of millions of arithmetic operations for a video sequence lasting a few seconds. To address these problems, we propose a heterogeneous approach that is based on an extensive algorithmic and experimental analysis of the histogram of gradients application. We divide the application into four stages and evaluate each on the CPU, GPU, and FPGA platforms. Our heterogeneous design combines the strengths of both the FPGA and GPU platforms, and achieves a $1.3X$ speedup compared with a state-of-the-art GPU while being $1.5X$ more energy efficient than other homogeneous solutions, including FPGA-based designs. Moreover, our heterogeneous HAR design using fixed-point arithmetic has comparable accuracy to those of HAR algorithms using single precision floating point arithmetic.

中文翻译：

---

HAR 应用的异构加速

人体动作识别 (HAR) 是一个重要的研究领域，涉及图像处理、计算机视觉和快速算法设计等领域。HAR 有几个重要的应用，包括医疗保健监控、安全和监视、辅助生活、智能家居以及视频搜索和索引。尽管该领域最近取得了进展，但仍然存在重大挑战。例如，HAR 在计算上是昂贵的。视频预处理、特征提取、特征量化和特征分类等任务需要对持续几秒钟的视频序列执行数百万次算术运算。为了解决这些问题，我们提出了一个异质该方法基于梯度应用直方图的广泛算法和实验分析。我们将应用程序分为四个阶段，并在 CPU、GPU 和 FPGA 平台上对每个阶段进行评估。我们的异构设计结合了 FPGA 和 GPU 平台的优势，实现了 $1.3X$ 与最先进的 GPU 相比加速，同时 $1.5X$ 比其他同类解决方案（包括基于 FPGA 的设计）更节能。此外，我们使用定点算法的异构 HAR 设计与使用单精度浮点算法的 HAR 算法具有相当的准确性。