May internship challenge: Estimating Distance between Two Balls App
•Download as PPTX, PDF•
0 likes•34 views
This is the project developed by Ikuto Oki at the end of his internship at Ridge-i.
Recommended
More Related Content
What's hot
What's hot (10)
Similar to May internship challenge: Estimating Distance between Two Balls App
Similar to May internship challenge: Estimating Distance between Two Balls App (13)
More from Ridge-i, Inc.
More from Ridge-i, Inc. (8)
Recently uploaded
Recently uploaded (20)
May internship challenge: Estimating Distance between Two Balls App
- 1. Internship Challenge Presentation: Estimating Distance between Two Balls App Author: IKUTO OKI Date: 2021/06/18
- 2. May Internship Challenge Estimating Distance between Two Balls App
- 3. Overview An application for estimating the distance between two balls from a monocular RGB image 3 ball ball distance
- 4. Overview 4 There is no restriction on the direction of image capture
- 5. Overview 5 Goal : estimate distance between two balls distance There are no restriction on the direction of image capture
- 6. Base technology & theory 6 Semantic segmentation why : estimating area of each ball When the distance between objects increases by x, the apparent size increases by 1/x2 ・ to estimate the distance to the ball from the area of the ball Triangle congruence theorems why : triangulate the distance between two balls from the distance between ball and camera
- 7. Method -overview- 7 Triangle congruence theorem needs 𝐿1:distance between camera and ball 𝐿2:distance between camera and ball 𝜃:angle between ball1, ball2, and camera 𝜃 𝐿1 𝐿2 𝐷 ball1 ball2 𝐿1, 𝐿2, 𝜃 → 𝐷
- 8. Method -overview- 8 Triangle congruence theorem needs 𝐿1:distance between camera and ball 𝐿2:distance between camera and ball 𝜃:angle between ball1, ball2, and camera 𝜃 𝐿1 𝐿2 𝐷 ball1 ball2 prior information ・camera angle of view ・number of pixels when the ball is at the reference distance 𝐿1, 𝐿2, 𝜃 → 𝐷
- 9. Method -estimating distance between ball and camera- 9 𝜃 𝐿1 𝐿2 𝐷 ball1 ball2 Triangle congruence theorem needs 𝐿1:distance between camera and ball 𝐿2:distance between camera and ball 𝜃:angle between ball1, ball2, and camera
- 10. Method -estimating distance between ball and camera- 10 1 : Calibrating distance of the ball (prior information) · count the number of pixels when the ball is at the reference distance · use five annotation images 2 : Calculating the area of each ball · semantic segmentation · labeling two balls using K-MEANS 3 : Calculating the distance between ball and camera · use law of distance 𝑆0 1 𝑥2 ⇄ 𝐿0𝑥
- 11. 1 : Calibrating distance of the ball (prior information) · count the number of pixels when the ball is at the reference distance · use five annotation images 2 : Calculate the area of each ball · semantic segmentation · rabeling two balls using K-MEANS 3 : Calculating the distance between ball and camera · use law of distance 𝑆0 1 𝑥2 ⇄ 𝐿0𝑥 Method -estimating distance between ball and camera- Ball distance=0.5[m](= 𝐿0) annotation image X0[pixels] U [pixels] V [pixels] S0=X0/(U2+V2) 11
- 12. Method -estimating distance between ball and camera- 12 1 : Calibrating distance of the ball (prior information) · count the number of pixels when the ball is at the reference distance · use five annotation images 2 : Calculating the area of each ball · semantic segmentation · labelling two balls using K-MEANS 3 : Calculating the distance between ball and camera · use law of distance 𝑆0 1 𝑥2 ⇄ 𝐿0𝑥 output of semseg output of K-MEANS
- 13. 1 : Calibrating distance of the ball (prior information) · count the number of pixels when the ball is at the reference distance · use five annotation images 2 : Calculating the area of each ball · semantic segmentation · labelling two balls using K-MEANS 3 : Calculating the distance between ball and camera · use law of distance 𝑆0 1 𝑥2 ⇄ 𝐿0𝑥 Method -estimating distance between ball and camera- 13 𝑥:scale factor, 𝐿𝑥: 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡ℎ𝑎𝑡 𝑠𝑐𝑎𝑙𝑒 𝑓𝑎𝑐𝑡𝑜𝑟 𝑖𝑠 𝑥 1 𝑥2 = 𝑆𝑥 𝑆0 , 𝑆0 1 𝑥2 ⇄ 𝐿0𝑥 (= 𝐿𝑥)より 𝐿𝑥 = 𝐿0 𝑆0 𝑆𝑥
- 14. Method -estimate angle between ball1, ball2, and camera- 14 𝜃 𝐿1 𝐿2 𝐷 ball1 ball2 Triangle congruence theorems needs 𝐿1:distance between camera and ball 𝐿2:distance between camera and ball 𝜃:angle between ball1, ball2, and camera
- 15. 15 Method -estimate angle between ball1, ball2, and camera- 𝜃 Ball1(𝑢1, 𝑣1) ball2 (𝑢2, 𝑣2) 𝛼: vertical angle 𝛽: horizontal angle Find the Euclidean distance and normalize by the angle of view ▶︎ estimate 𝜃 = 𝐴 × 𝐵 𝐶 𝐵 = (𝑢1 − 𝑢2)2 + (𝑢1−𝑢2)2 𝐴 = 𝛼2 + 𝛽2 𝐶 = 𝑈𝑚𝑎𝑥 2 + 𝑉 𝑚𝑎𝑥 2
- 16. 16 Method -triangle congruence theorems - Triangle congruence theorems needs 𝐿1:distance between camera and ball 𝐿2:distance between camera and ball 𝜃:angle between ball1, ball2, and camera 𝜃 𝐿1 𝐿2 𝐷 ball1 ball2 <repost>
- 17. 17 Method -triangle congruence theorems - Triangle congruence theorems needs 𝐿1:distance between camera and ball 𝐿2:distance between camera and ball 𝜃:angle between ball1, ball2, and camera 𝜃 𝐿1 𝐿2 𝐷 ball1 ball2 <repost> 𝐷= 𝐿1 2 + 𝐿2 2 − 2𝐿1𝐿2𝑐𝑜𝑠𝜃 (Law of cosines)
- 18. Result 18 9905 9906 9908 9909 9910
- 19. Result 19 9906 9905 9908 9909 9910
- 20. Discussion1 20 No.1 possible source of error is the accuracy of semseg → Increase annotation images and new ways to learn 9906 Correct distance : 50[cm] Output distance 345[cm]
- 21. Discussion2 21 Photo distortion may affect accuracy →Obtaining internal parameters and correcting [1],[2]http://www.tctec.co.jp/solution/img_pro/camera/ Distortion[1] No distortion[2]
- 22. Application, future potential 22 All this program needs are balls and a monocular visible light camera. Because it is simple to use, it can be installed on Smartphone. Program only needs one direction to work K-MEANS (k=2) was used, but another algorithm(e.g. X-MEANS) can be used to estimate if more than three balls are included
- 23. Thank you for listening!