인텔의 OpenVINO Toolkit의 샘플을 돌려보았다.

1. OpenVINO™ Toolkit
2018.09.08
김정훈 jhkim@dilab.co.kr

2. Goal: LPR problem
 License Plate Recognition
 License plate detection
 OCR (Optical character recognition)
 Templet matching
 Deep learning
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3. Goal: LPR problem
 Templet matcing
 ROI영역 내 번호판 영역 검지
 Image 변환후 각 문자 segment 추출
 Templet matching
 Pros
 빠른속도
 적은 자원소모
 Embedded system, Mobile system
 Cons
 인식률의 한계
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4. Goal: LPR problem
 using Deep learning
 Object Detection
 OCR
 Pros
 인식률의 향상가능성
 다양한 상황에서 비교적 robust
 Cons
 높은 자원 요구
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5. OpenVINO™ Toolkit
 Open Visual Inference & Neural Network Optimization
 Intel software
 다양한 System Platform 지원
 CPU, GPU, FPGA, IPU, OS…
 Via OpenCV
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6. Security Barrier Camera Sample
 vehicle-license-plate-detection
 primary detection network to find the vehicles and licence-plates
 vehicle-attributes-recognition
 reports the general vehicle attributes like type (car/van/bus/track)
and color
 license-plate-recognition
 reports a string per recognized license-plate
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7. vehicle-license-plate-detection
 vehicle-license-plate-detection example
 ResNet10
 SSD-based vehicle and (Chinese) license plate detector
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8. vehicle-license-plate-detection
 Specification
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9. vehicle-license-plate-detection
 Input
 name: "input" , shape: [1x3x256x320]
 input image in the format [BxCxHxW]
 B - batch size
 C - number of channels
 H - image height
 W - image width
 Output
 Net outputs blob with shape: [1, 1, N, 7]
 N is the number of detected bounding boxes.
 For each detection, the description has the format:
[image_id, label, conf, x_min, y_min, x_max, y_max]
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10. vehicle-attributes-recognition
 vehicle-attributes-recognition example
 ResNet10
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11. vehicle-attributes-recognition
 Specification
 Accuracy
 Color average accuracy = 82.04%
 Type average accuracy = 87.70%
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12. license-plate-recognition
 license-plate-recognition
 ~10K Chinese plates from different provinces
 small-footprint network
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13. license-plate-recognition
 small-footprint network
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14. license-plate-recognition
 Specification
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15. Security Barrier Camera Sample
 code
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16. Security Barrier Camera Sample
 result
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17. Paper
 Stereo Camera : fail
 siamese network, w-net, SGM…
 Yolo v3, SSD, Faster-RCNN2 …
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Deep LPR System more accurate results
Higher level of data
(depth data)

18. Q&A
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