數(shù)字圖像處理——疲勞檢測(cè)

1、,,,,數(shù)字圖像處理-疲勞檢測(cè),姓名：xxx計(jì)算機(jī)科學(xué)技術(shù)學(xué)院,總覽,Landmark+XGBoostLandmark+LSTMLandmark+BiLSTMLandmark+BiLSTM+Attention,,Baseline,SingleFram+LandmarkVGG-like CNNDenseNetC3DLRCN,,Method,Landmark+XGBoost,人臉關(guān)鍵點(diǎn)提取,人臉關(guān)鍵點(diǎn)提取,主要方法ASM

2、（Active Shape Model） 基于級(jí)聯(lián)形狀回歸 基于深度學(xué)習(xí),實(shí)驗(yàn)中One Millisecond Face Alignment with an Ensemble of Regression TreesiBUG 300-W 訓(xùn)練集,,眼睛大小,疲憊時(shí)眨眼、閉眼,01,,嘴巴大小,哈欠時(shí)張大嘴巴,02,,點(diǎn)頭估計(jì),瞌睡時(shí)小雞啄米,03,P80、P70、EM,,眼睛,張開(kāi)程度極大值張開(kāi)超過(guò)閾值幀數(shù),,嘴巴,極值坐標(biāo)之差

3、標(biāo)準(zhǔn)差,,鼻尖,特征提取,特征提取,Max_mouth_size,mouth_size > alpha,yawn,normal,Landmark+XGBoost,XGBoost梯度提升決策樹(shù)高度靈活 收斂迅速,Landmark+RNN,LSTM,Landmark+RNN,Landmark+RNN,BiLSTM,LSTM with Attention,Baseline,單幀vs視頻？,Baseline,端到端vs人臉特征？,結(jié)

4、論,各模型測(cè)試集結(jié)果,XGBoost干擾項(xiàng)測(cè)試結(jié)果,結(jié)論,深度模型優(yōu)異,人臉特征與視頻連續(xù),魯棒性較強(qiáng),Pros and cons,Reference,[1] Dinges D F, Grace R. PERCLOS: A valid psychophysiological measure of alertness as assessed by psychomotor vigilance[J]. US Department of Tra

5、nsportation, Federal Highway Administration, Publication Number FHWA-MCRT- 98-006, 1998. [2] Wang Q, Yang J, Ren M, et al. Driver fatigue detection: a survey[C]//Intelligent Control and Automation, 2006. WCICA 2006. The

6、 Sixth World Congress on. IEEE, 2006, 2: 8587-8591. [3] Gu H, Ji Q, Zhu Z. Active facial tracking for fatigue detection[C]//Applications of Computer Vision, 2002.(WACV 2002). Proceedings. Sixth IEEE Workshop on. IEEE, 2

7、002: 137-142. [4] Pei Z, Zhenghe S, Yiming Z. Perclos-based recognition algorithms of motor driver fatigue[J]. Journal-China Agri- cultural University, 2002, 7(2): 104-109. [5] Dzmitry Bahdanau, Kyunghyun Cho, Yoshua B

8、engio: Neural Machine Translation by Jointly Learning to Align and Translate.ICLR 2015 [6] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin: Attent

9、ion is All you Need. NIPS 2017: 6000-6010 [7] Je Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell: Long-term Recurrent Convolutional Networks for Visu

10、al Recognition and Description. CVPR 2015 [8] Karen Simonyan, Andrew Zisserman:Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR abs/1409.1556 (2014) [9] Gao Huang, Zhuang Liu, Laurens van der Ma

11、aten, Kilian Q. Weinberger: Densely Connected Convolutional Net- works. CVPR 2017: 2261-2269,PPT模板下載：www.1ppt.com/moban/ 行業(yè)PPT模板：www.1ppt.com/hangye/ 節(jié)日PPT模板：www.1ppt.com/jieri/ PPT素材下載：www.1ppt.com/sucai/

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