基於時空域摺積神經網路之抽菸動作辨識

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姓名

邱千芳(Chien-Fang Chiu) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

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)

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摘要(中)

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。

摘要(英)

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%.

關鍵字(中)

★ 抽菸動作辨識
★ 視訊分類
★ 摺積神經網路
★ 深度學習

關鍵字(英)

★ Smoking action recognition
★ Video Classification
★ Convolutional neural networks
★ Deep learning

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章緒論 1
1-1 研究背景 1
1-2研究動機與目的 2
1-3 論文架構 3
第二章類神經網路與深度學習 4
2-1 類神經網路 4
2-1-1 類神經網路之發展 5
2-1-2 倒傳遞類神經網路 7
2-2 深度學習 11
2-2-1 深度神經網路 11
2-2-2 摺積神經網路 (CNN) 13
2-2-3 批次資料正規化 (Batch Normalization) 18
2-3 動作辨識領域之發展 22
2-3-1 雙串流的神經網路 (Two-stream networks) 22
2-3-2 Temporal segment networks (TSN) 23
第三章提出之方法與相關使用 26
3-1 影片影格之提取與資料前處理 27
3-2 訓練階段 28
3-3 測試階段 29
第四章實驗結果與分析 30
4-1 實驗環境 30
4-2 相關參數與資料選擇 33
4-2-1 預訓練模型與輸入網路選擇之實驗 34
4-2-2 資料增加之選擇 35
4-2-3 資料平衡 (data balancing) 後之實驗結果 36
4-2-4實驗結果比較與分析 39
第五章結論與未來展望 44
參考文獻 45

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指導教授

張寶基(Pao-Chi Chang)

審核日期

2018-7-25

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