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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/82363


    Title: 應用於人體姿勢辨識與機器人之可重組深度神經網路引擎-子計畫四:應用可重組深度神經網路技術之姿勢與行為辨識系統;Human Pose Estimation and Action Recognition System on Reconfigurable Dnn
    Authors: 蔡宗漢
    Contributors: 國立中央大學電機工程學系
    Keywords: 人工智慧;機器學習;影像處理;深度學習;神經網路;姿勢偵測;artificial intelligence;machine learning;image processing deep learning;neural network;human activity analysis
    Date: 2020-01-13
    Issue Date: 2020-01-13 14:48:03 (UTC+8)
    Publisher: 科技部
    Abstract: 人工智慧(AI)的技術在我們的生活中越來越常見,不管是語音辨識、人臉辨識或是物件分類等應用都有許多人在研究。傳統演算法無法擴展至複雜的情況已藉由深度神經網路經由CUDA運算核加速解決了,AI技術在許多應用上,已經有著優於人類決策或判斷的能力,所以AI也變成目前最熱門的研究領域。隨著越來越多可應用於生活上的研究發表,智慧家庭也不在是一個夢想,可以想像在未來的生活,家庭裡的各項電器都可以用語音操控,也會偵測人類行為來自動調整燈光強弱、冷氣溫度或是電視音量等等,使生活更便利。物聯網智慧家庭(Smart Home on IoT)是未來家庭主要的趨勢與潮流,在本計畫將使用低成本的彩色影像鏡頭,而非昂貴的紅外線偵測鏡頭(如: Kinect、realsense等),預期達到近乎即時處理並且貼近於人類生活與感知環境的系統,對於居家使用者也將不會擁有價格上昂貴的負擔,並能擁有便捷舒適的操控環境。在此我們將要探討如何使用兩個影像感測器所產生的影像透過上述技術來產生複雜場景下的深度影像圖,不需要穿戴式設備或預先錄製場景再設置系統的做法,並探討如何使用立體影像視覺技術來轉換為具代表性的人體部位特徵,以利於其他研究使用至姿勢判斷及其他居家照護與智慧家庭的應用。 ;The technology and application of Artificial intelligence is become more and more common in our lives, whether it is the speech recognition, face recognition or object classification applications have a lot of research. The inability of traditional algorithms to extend to complex environments has been solve by deep neural networks via cuda cores accelerating. AI technology is superior to human on decision-making or judgment in many applications, therefore, AI has become the hottest research area. With more and more research that can be applied to real life published, the smart home is no longer a dream. We can imagine life in the future, appliances in house can be controlled by voice, and human behavior can be detected to automatically adjust light levels, air-conditioning temperatures, or tv volume, making life more convenient. Smart home is the main trend in the future, in this project, low-cost color image cameras will be used instead of expensive infrared cameras (Kinect, Realsense, etc.). The system is expected to achieve real-time processing and is similar to the real-life environment, for ordinary users, there will be a convenient and comfortable operating environment and no price burden. In this project, we will discuss how to use two image cameras to generate the depth images in complex environments without wearable devices requiring or pre-setting system. And explore how to use stereo vision technology to transform the dual camera stream into representative body parts features, which other researches can use to action recognition, health care or smart home application.
    Relation: 財團法人國家實驗研究院科技政策研究與資訊中心
    Appears in Collections:[電機工程學系] 研究計畫

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