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DeNA TechCon 2020
#denatechcon
DeNA TechCon 2020
DRIVE CHART
DeNA TechCon 2020
#denatechcon
Tomoyuki Suzuki
AI
• AI CV
• DRIVE CHART
• ~2019.3
• 2019.4 DeNA
@tomoyukun
DeNA TechCon 2020
#denatechcon
(CV: Computer Vision)
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DRIVE CHART
AI
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@Takarasawa_
AI
• AI CV
• DRIVE CHART
• ~2019.3
• 2019.4 DeNA
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コンピュータビジョン技術の実応用〜DRIVE CHARTにおける脇見・車間距離不足検知〜【DeNA TechCon 2020 ライブ配信】

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本発表ではコンピュータビジョン技術の実応用例として『DRIVE CHART』におけるドラレコ動画からの危険シーンの検知、特に脇見・車間距離不足検知システムについてご紹介します。

Published in: Technology
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コンピュータビジョン技術の実応用〜DRIVE CHARTにおける脇見・車間距離不足検知〜【DeNA TechCon 2020 ライブ配信】

  1. 1. DeNA TechCon 2020 #denatechcon DeNA TechCon 2020 DRIVE CHART
  2. 2. DeNA TechCon 2020 #denatechcon Tomoyuki Suzuki AI • AI CV • DRIVE CHART • ~2019.3 • 2019.4 DeNA @tomoyukun
  3. 3. DeNA TechCon 2020 #denatechcon (CV: Computer Vision) dog 2.2. Prior art Much of the work on GAN architectures has focuse on improving the discriminator by, e.g., using multip discriminators [18, 47, 11], multiresolution discriminatio [60, 55], or self-attention [63]. The work on generator sid has mostly focused on the exact distribution in the input l tent space [5] or shaping the input latent space via Gaussia mixture models [4], clustering [48], or encouraging conve ity [52]. Recent conditional generators feed the class identifi through a separate embedding network to a large numb of layers in the generator [46], while the latent is still pr vided though the input layer. A few authors have considere feeding parts of the latent code to multiple generator laye [9, 5]. In parallel work, Chen et al. [6] “self modulate” th generator using AdaINs, similarly to our work, but do n consider an intermediate latent space or noise inputs. * *Karras et al., “A Style-Based Generator Architecture for GeneraCve,” in Proc. of CVPR 2019. cat
  4. 4. DeNA TechCon 2020 #denatechcon DRIVE CHART AI • 2019 6 4 • AI DRIVE CHART
  5. 5. DeNA TechCon 2020 #denatechcon DRIVE CHART CV
  6. 6. DeNA TechCon 2020 #denatechcon 1. 2. 3. 4.
  7. 7. DeNA TechCon 2020 #denatechcon 1. 2. 3. 4.
  8. 8. DeNA TechCon 2020 #denatechcon % 0 ( ) ) % 67 8 % 67 8 5 8 2 1 %3 .
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  15. 15. DeNA TechCon 2020 #denatechcon • • 3 3 D Zhang +, OpenGaze Demo: Gaze Visualiza7on: h8ps://www.youtube.com/watch?v=9Lujg3beiYI 3
  16. 16. DeNA TechCon 2020 #denatechcon • • Screen coordinate system Camera coordinate system Head coordinate system Figure 1: System configuration for data collection LCD monitor, and these cameras capture images in a syn- chronized manner via a software trigger controlled by the host computer. Intrinsic and extrinsic camera parameters are calibrated beforehand, and the 3D position of the moni- Midpoints of 3D facial landmarks Figure 2: Definition of head pose. The head coordinate sys tem is defined based on a triangle connecting three mid points of the eyes and mouth. poses of the subjects. As illustrated in Fig. 2, the head coor Y. Sugano et al., ”Learning-by-Synthesis for Appearance-Based 3D Gaze EsBmaBon,” in Proc. of CVPR, 2014 Andreas et al., "Wearable EOG goggles: Eye-based interacBon in everyday environments," in Proc. of CHI 2009.
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  36. 36. DeNA TechCon 2020 #denatechcon (RNN: Recurrent Neural Network) !"!#!$
  37. 37. DeNA TechCon 2020 #denatechcon (RNN: Recurrent Neural Network) !"!#!$
  38. 38. DeNA TechCon 2020 #denatechcon (RNN: Recurrent Neural Network) !"!#!$
  39. 39. DeNA TechCon 2020 #denatechcon (RNN: Recurrent Neural Network) !"!#!$
  40. 40. DeNA TechCon 2020 #denatechcon (RNN: Recurrent Neural Network) 0.8 0.2 0.6
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  46. 46. DeNA TechCon 2020 #denatechcon – - -
  47. 47. DeNA TechCon 2020 #denatechcon Takumi Karasawa @Takarasawa_ AI • AI CV • DRIVE CHART • ~2019.3 • 2019.4 DeNA
  48. 48. DeNA TechCon 2020 #denatechcon 1. 2.
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  76. 76. DeNA TechCon 2020 #denatechcon DRIVE CHART DRIVE CHART AI AI CV
  77. 77. DeNA TechCon 2020 #denatechcon DeNA TechCon 2020

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