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    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/77404


    題名: 基於時空域摺積神經網路之抽菸動作辨識;Smoking Action Recognition Based on Spatial-Temporal Convolutional Neural Networks
    作者: 邱千芳;Chiu, Chien-Fang
    貢獻者: 通訊工程學系
    關鍵詞: 抽菸動作辨識;視訊分類;摺積神經網路;深度學習;Smoking action recognition;Video Classification;Convolutional neural networks;Deep learning
    日期: 2018-07-25
    上傳時間: 2018-08-31 14:37:07 (UTC+8)
    出版者: 國立中央大學
    摘要: 國際上有許多國家或各區於室內公共或工作場所全面禁止抽菸,台灣也不例外。但在醫院的門口、校園的角落,仍時常看到有人在抽菸。即使沒有吸菸,但若站在吸菸者旁邊,仍會吸到菸,此菸稱為二手菸。二手菸對於人體危害甚多,除了增加罹患疾病的機率,如癌症、心臟病、中風、呼吸道疾病等,更進一步有可能傷害大腦機能。我們希望經由深度學習的技術與方法,用以辨識揪出違法的吸菸者。
    本研究為「基於時空域摺積神經網路之抽菸動作辨識」,提出應用於抽菸動作辨識的系統。採用資料平衡與資料增加等方式增加效能,使用深度學習中的摺積神經網路 GoogLeNet,與Temporal segment networks之影片分段架構,組成擁有時間結構之空間域摺積神經網路(即題目之時空域神經網路),達成有效辨識抽菸影片之系統。於原先之 Hmdb51 抽菸影片,辨識達100%,於增加之 Activitynet smoking 日常抽菸影片 (Hmdb51 + Activi-tynet smoking),可達99.16%。於選擇之 AVA data 電影抽菸片段,亦能達到91.667%,能有效分辨抽菸之影片。
    ;Cigarette smoking increases risk for death from all causes in men and wom-en. If one stands next to a smoker, this person still can be infected, called passive smoking. Consequently, smoking is prohibited in many closed public areas such as government buildings, educational facilities, hospitals, enclosed sport facili-ties, and buses. However, it still often happens that smokers smoke even in highly prohibited places such as hospitals and elementary school campuses. The objective of this work is to develop a smoking action recognition system based on deep learning, which allows quick discovery of smoking behavior.
    In this work, we propose a system that can recognize smoking action. It uti-lizes data balancing and data augmentation based on GoogLeNet and Temporal segment networks (TSN) architecture to achieve effective smoking action recog-nition. In our experiment, spatial CNN is more powerful than temporal CNN in smoking action. The experimental results show that the smoking accuracy rate can reach 100% for Hmdb51 test dataset. For additional ActivityNet smoking, accuracy rate can reach 99.16%. For additional irrelevant movie smoking clips, the accuracy can also be as high as 91.67%.
    顯示於類別:[通訊工程研究所] 博碩士論文

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