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研究生:簡瑞霆
研究生(外文):Chien, Jui-Ting
論文名稱:隱「行」人偵測
論文名稱(外文):Detecting nonexistent pedestrians
指導教授:陳煥宗
指導教授(外文):Chen, Hwann-Tzong
口試委員:賴尚宏劉庭祿
口試委員(外文):Lai, Shang-HongLiu, Tyng-Luh
口試日期:2017-06-29
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:43
中文關鍵詞:物件偵測語意分割深度學習卷機神經網路對抗式生成網路
外文關鍵詞:Object detectionSemantic segmentaitionDeep learningConvolutional neural networkGenerative adversarial networks
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有別於一般的物件偵測跟語意分割等視覺問題,本論文希望藉由進一步分析不存在的行人出現於道路街景中的機率,來探討利用周邊資訊間接達成場景感知的可能性。我們的方法建立於生成和鑑別的對抗流程上,藉此實現具備找出遺失的視覺資訊的感知能力。為了產生可用於偵測不存在行人的訓練資料,我們使用了最新的圖像修補技術,即使圖片中的指定區域只存在背景,經過學習後的偵測器依然可以預測該區域觀測到行人的機率。我們根據存在機率將額外的行人放入對應圖片中,再透過使用者研究來評估我們的方法,衡量合成的圖片和真實圖片是否已難以區別。實證研究的結果顯示,我們的方法可以領悟到在道路街景中,何處是合理的行人走路或站立位置的概念。
We explore beyond object detection and semantic segmentation, and propose to address
the problem of estimating the presence probabilities of nonexistent pedestrians in a street
scene. Our method builds upon a combination of generative and discriminative procedures
to achieve the perceptual capability of figuring out missing visual information. We adopt
state-of-the-art inpainting techniques to generate the training data for nonexistent pedestrian
detection. The learned detector can predict the probability of observing a pedestrian
at some location in the current image, even if that location exhibits only the background.
We evaluate our method by inserting pedestrians into the image according to the presence
probabilities and conducting user study to distinguish real and synthetic images. The empirical
results show that our method can capture the idea of where the reasonable places are
for pedestrians to walk or stand in a street scene.
1 Introduction 9
2 Related work 11
3 Approach 13
3.1 Training Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.3 Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4 Experiments 25
4.1 Nonexistent Pedestrian Detection . . . . . . . . . . . . . . . . . . . . . . . 25
4.2 Synthetic Images with Rendered Pedestrians . . . . . . . . . . . . . . . . . 28
5 Conclusion and Future Work 36
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