A Light-powered, "Always-On", Smart Camera with Compressed Domain Gesture Detection

Proceedings of the 2016 International Symposium on Low Power Electronics and Design Pub Date : 2016-05-26 DOI:10.1145/2934583.2934594

A. Anvesha, Shaojie Xu, N. Cao, J. Romberg, A. Raychowdhury

{"title":"A Light-powered, \"Always-On\", Smart Camera with Compressed Domain Gesture Detection","authors":"A. Anvesha, Shaojie Xu, N. Cao, J. Romberg, A. Raychowdhury","doi":"10.1145/2934583.2934594","DOIUrl":null,"url":null,"abstract":"In this paper we propose an energy-efficient camera-based gesture recognition system powered by light energy for \"always on\" applications. Low energy consumption is achieved by directly extracting gesture features from the compressed measurements, which are the block averages and the linear combinations of the image sensor's pixel values. The gestures are recognized using a nearest-neighbour (NN) classifier followed by Dynamic Time Warping (DTW). The system has been implemented on an Analog Devices Black Fin ULP vision processor and powered by PV cells whose output is regulated by TI's DC-DC buck converter with Maximum Power Point Tracking (MPPT). Measured data reveals that with only 400 compressed measurements (768x compression ratio) per frame, the system is able to recognize key wake-up gestures with greater than 80% accuracy and only 95mJ of energy per frame. Owing to its fully self-powered operation, the proposed system can find wide applications in \"always-on\" vision systems such as in surveillance, robotics and consumer electronics with touch-less operation.","PeriodicalId":142716,"journal":{"name":"Proceedings of the 2016 International Symposium on Low Power Electronics and Design","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 International Symposium on Low Power Electronics and Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2934583.2934594","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 9

Abstract

In this paper we propose an energy-efficient camera-based gesture recognition system powered by light energy for "always on" applications. Low energy consumption is achieved by directly extracting gesture features from the compressed measurements, which are the block averages and the linear combinations of the image sensor's pixel values. The gestures are recognized using a nearest-neighbour (NN) classifier followed by Dynamic Time Warping (DTW). The system has been implemented on an Analog Devices Black Fin ULP vision processor and powered by PV cells whose output is regulated by TI's DC-DC buck converter with Maximum Power Point Tracking (MPPT). Measured data reveals that with only 400 compressed measurements (768x compression ratio) per frame, the system is able to recognize key wake-up gestures with greater than 80% accuracy and only 95mJ of energy per frame. Owing to its fully self-powered operation, the proposed system can find wide applications in "always-on" vision systems such as in surveillance, robotics and consumer electronics with touch-less operation.

查看原文本刊更多论文

具有压缩域手势检测功能的光能、“永远在线”智能相机

在本文中，我们提出了一种节能的基于摄像头的手势识别系统，该系统由光能驱动，用于“永远打开”的应用。通过直接从压缩测量中提取手势特征，即图像传感器像素值的块平均和线性组合，实现了低能耗。手势识别使用最近邻(NN)分类器，然后是动态时间扭曲(DTW)。该系统已在Analog Devices的Black Fin ULP视觉处理器上实现，并由光伏电池供电，其输出由TI的DC-DC降压转换器调节，具有最大功率点跟踪(MPPT)。实测数据表明，每帧只有400个压缩测量(768倍压缩比)，系统能够以超过80%的准确率识别关键唤醒手势，每帧只有95mJ的能量。由于其完全自供电的操作，所提出的系统可以在“永远在线”的视觉系统中找到广泛的应用，例如监控，机器人和无触摸操作的消费电子产品。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Proceedings of the 2016 International Symposium on Low Power Electronics and Design

自引率

0.00%

发文量