Pattern classification applications can be found everywhere, especially the ones that use computer vision. What makes them difficult to embed is the fact that they often require a lot of computational resources. Embedded computer vision has been applied in many contexts, such as industrial or home automation, robotics, and assistive technologies. This work performs a design space exploration in an image classification system and embeds a computer vision application into a minimum resource platform, targeting wearable devices. The feature extractor and the classifier are evaluated for memory usage and computation time. A method is proposed to optimize such characteristics, leading to a reduction of over 99% in computation time and 92% in memory usage, with respect to a standard implementation. Experimental results in an ARM Cortex-M platform showed a total classification time of 0.3 s, maintaining the same accuracy as in the simulation performed. Furthermore, less than 20 KB of data memory was required, which is the most limited resource available in low-cost and low-power microcontrollers. The target application, used for the experimental evaluation, is a crosswalk detector used to help visually impaired persons.

模式分类应用程序随处可见，尤其是使用计算机视觉的应用程序。使它们难以嵌入的原因是它们经常需要大量的计算资源。嵌入式计算机视觉已被应用于许多场合，例如工业或家庭自动化，机器人技术和辅助技术。这项工作在图像分类系统中进行了设计空间探索，并将计算机视觉应用程序嵌入到针对可穿戴设备的最小资源平台中。对特征提取器和分类器的内存使用情况和计算时间进行评估。提出了一种优化这些特性的方法，相对于标准实现，该方法可将计算时间减少超过99％，将内存使用减少92％。在ARM Cortex-M平台上的实验结果表明，总分类时间为0.3 s，保持与执行的仿真相同的准确性。此外，所需的数据存储器少于20 KB，这是低成本和低功耗微控制器中可用的最有限的资源。用于实验评估的目标应用是用于帮助视力障碍者的人行横道检测器。