| DC 欄位 |
值 |
語言 |
| DC.contributor | 通訊工程學系 | zh_TW |
| DC.creator | 邱千芳 | zh_TW |
| DC.creator | Chien-Fang Chiu | en_US |
| dc.date.accessioned | 2018-7-25T07:39:07Z | |
| dc.date.available | 2018-7-25T07:39:07Z | |
| dc.date.issued | 2018 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=104523036 | |
| dc.contributor.department | 通訊工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 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。 | zh_TW |
| dc.description.abstract | 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%. | en_US |
| DC.subject | 抽菸動作辨識 | zh_TW |
| DC.subject | 視訊分類 | zh_TW |
| DC.subject | 摺積神經網路 | zh_TW |
| DC.subject | 深度學習 | zh_TW |
| DC.subject | Smoking action recognition | en_US |
| DC.subject | Video Classification | en_US |
| DC.subject | Convolutional neural networks | en_US |
| DC.subject | Deep learning | en_US |
| DC.title | 基於時空域摺積神經網路之抽菸動作辨識 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Smoking Action Recognition Based on Spatial-Temporal Convolutional Neural Networks | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |