メテオサーチチャレンジ発表会資料(3位)

1. PERC

2. •
• (HEVC,H.264,AAC,Opus, )
•
•

3. 0
0.25
0.5
0.75
1
4/8 4/18 4/28 5/8 5/20 6/2 6/30
0.80495
0.88908
0.88856
0.89988
0.83184
0.86017
0.92017
0.92671
0.99791
0.97547
0.99930
0.99436

4. •
(
)
•
•

5. •
z
1 2 3
|1-2| |2-3|

6. 000029.mpg
while True:
ret, frame = cap.read()
if not ret:
break
gray = cv2.cvtColor(frame, toGray)
diff = cv2.absdiff(gray, prev_gray)
sum_diff += diff
prev_gray = gray
sum_diff[sum_diff[:,:] > 255] = 0
while True:
ret, frame = cap.read()
if not ret:
break
gray = cv2.cvtColor(frame, toGray)
diff = cv2.absdiff(gray, prev_gray)
diff[diff[:,:] < 24] = 0 #
sum_diff += diff
prev_gray = gray
sum_diff[sum_diff[:,:] > 255] = 0

7. SSD
(Single Shot MultiBox Detector)
200 178
4 18
0.80495
0.88908
https://github.com/slowsingle/labeling_for_object_detection
https://github.com/rykov8/ssd_keras

8. • 24 0
•
009888.mpg
24 0 8 0
4 28
0.88856
0.89988

9. •
•
•

10. •
while True:
ret, frame = cap.read()
if not ret:
break
gray = cv2.cvtColor(frame, toGray)
max = np.maximum(max, gray)
1
009888.mpg000029.mpg 000147.mpg

11. 3. 4.
002795.mpg
000029.mpg
000040.mpg
1.
2.

12. • RGB3ch
1ch
[ 2. 158. 0. 39. 155. 0. 54. 158. 0. 150. 150. 149. 0.]
[104. 0. 0. 0. 0. 0. 0. 53. 0. 150. 150. 149. 0.]
[104. 0. 2. 103. 0. 0. 104. 107. 147. 149. 150. 149. 0.]
[ 0. 0. 0. 158. 0. 0. 0. 0. 147. 147. 147. 147. 147.]
[ 0. 103. 29. 155. 104. 53. 0. 0. 147. 147. 147. 147. 147.]
[ 0. 0. 0. 0. 0. 0. 0. 145. 147. 147. 147. 147. 0.]
[145. 0. 0. 0. 0. 0. 0. 145. 147. 147. 147. 0. 0.]
[145. 0. 0. 0. 0. 0. 145. 145. 146. 147. 147. 0. 0.]
[145. 0. 0. 0. 0. 0. 145. 145. 146. 145. 0. 0. 0.]
[145. 0. 0. 0. 0. 0. 145. 145. 146. 145. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 145. 145. 146. 145. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 145. 145. 146. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 145. 146. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 143. 143. 143. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 143. 143. 143. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 143. 143. 143. 0. 0. 0. 0. 0. 0.]
[ 1. 0. 0. 0. 0. 143. 143. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 143. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 141. 141. 143. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 143. 141. 141. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 143. 141. 141. 141. 0. 0. 0. 0. 0. 0. 0.]
141 150
20
0

13. 194 250
R.
( )
G.
( 24 0)
B.
SSD
1.
2.
3.
4.
5 8
0.83184
0.86017

14. YOLOv3
(You Only Look Once)
• SSD YOLOv3
5 20
0.92017
0.92671
https://github.com/experiencor/keras-yolo3
R.
( )
G.
( 24 0)
B.
YOLOv3
1.
2.
3.
4.

15. •
1
( )
n1 n2 n3 n4
L
(n2-n1) / L
(n3-n2) / L
(n4-n3) / L
≒2.0
/
2
R:
G:
B:

16. 2
( )
•
•
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 143. 144. 143. 0. 0. 0. 0.]
[ 0. 0. 0. 144. 144. 144. 0. 0. 0. 0.]
[ 0. 0. 0. 143. 143. 144. 144. 0. 0. 0.]
[ 0. 0. 0. 0. 144. 144. 143. 0. 0. 0.]
[ 0. 0. 0. 0. 143. 143. 144. 144. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 143. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
002597.mpg
0

17. •
•
3
( )
/
/
/
/
> 0.8
< 0.2

18. •
cv2.createCLAHE(
clipLimit=6.0, tileGridSize=(8,8))

19. 6 2
0.99791
0.97547
R.
(
)
G.
( )
B.
YOLO
v3
1.
2.
3.
4.

20. •
• 100 0

21. Non-local Means Filter
• Non-
local Means Filter
NLMeansFilter
cv2.fastNlMeansDenoising(frame,None,3,7,21)

22. R.
(
)
G.
( 100 0
NLMeansFilter+
)
B.
( 100 0)
YOLO
v3
1.
2.
3.
4.
6 30
0.99930 (2 )
0.99436 (3 )
194 250