This document discusses computer vision techniques for autonomous vehicles. It describes how computer vision can be used to detect lane lines, objects, and road signs/lights in order to solve problems for autonomous vehicles. It also discusses different levels of autonomous vehicles and provides examples of lane line detection and road sign identification algorithms using techniques like grayscale conversion, Sobel filtering, Hough transforms, and convolutional neural networks. Finally, it summarizes methods for object detection, tracking, and computing steering angles that are important for autonomous vehicle navigation and control.

1. Computer Vision for
Autonomous Vehicles
2020.07.22
Seowoo Han

2. Automatous Vehicles (AV)
Problems to solve
using computer
vision
1. Lane Line Detection
2. Object & Road
Signs/Light
Detection
3. Steering Angle
Computation

3. Automatous Vehicle levels
Janai, Joel, et al. "Computer vision for autonomous vehicles: Problems, datasets and state of
the art." Arxiv (2017): arXiv-1704.

4. Camera for Automatous Vehicles @Tesla
Multimodal Sensor
https://www.tesla.com/ko_KR/autopilot?redirect=no

5. Automatous Vehicles of Tesla, 2016
https://www.tesla.com/ko_KR/autopilot?redirect=no

6. Lane Line Detection
(A) RGB → Grayscale,
(Gaussian) smoothing
filter
(B) Sobel operator,
threshold
(C) Apply a mask to keep
the lane in the center of
the images
(D) Hough transform
Gandikota, Rohit. "Computer Vision for Autonomous Vehicles." arXiv preprint arXiv:1812.02542 (2018).

7. Lane Line Detection
(A) RGB → Grayscale,
(Gaussian) smoothing
filter
(B) Sobel operator,
threshold
(C) Apply a mask to keep
the lane in the center of
the images
(D) Hough transform
Gandikota, Rohit. "Computer Vision for Autonomous Vehicles." arXiv preprint arXiv:1812.02542 (2018).

8. Road Sign Identification
CNN을 이용한 road sign identification
(Basic problem)
3개의 convolution layers와 하나의
pooling layer의 결과를 fully-connected
layer로 연결 후, output 얻음
정확도: 99%~100%
Gandikota, Rohit. "Computer Vision for Autonomous Vehicles." arXiv preprint arXiv:1812.02542 (2018).

9. Object Detection paper list
Image Recognition vs. Detection
Recognition: object가 어떤 것인지 구분
Detection: object의 존재의 유무만 판단
https://github.com/hoya012/deep_learning_object_detection

10. 2D Object Recognition(YOLO v3(You Only Look Once v3))
Sliding window
Grid
Yolo v3 result →

11. Object Tracking
Object tracking flow chart
과속 카메라-현재 차량의 속도 파악
Parekh, Himani S., Darshak G. Thakore, and Udesang K. Jaliya. "A survey on object detection
and tracking methods." International Journal of Innovative Research in Computer and
Communication Engineering 2.2 (2014): 2970-2979.

12. Object Tracking
Object tracking methods
1. Point Tracking
움직이는 객체의 특징점을 찾는 방법이다.
2. Kernel Tracking
일정 영역 내부에 있는 움직임을 찾는 방법이다.
3. Silhouette Tracking
복잡한 형태를 단순화(실루엣) 시켜 움직임을 찾는 방
법이다.
Parekh, Himani S., Darshak G. Thakore, and Udesang K. Jaliya. "A survey on object detection
and tracking methods." International Journal of Innovative Research in Computer and
Communication Engineering 2.2 (2014): 2970-2979.

13. Steering Angle Computation
R. Rochan M., A. Alagammai K. and S. J., "Computer Vision Based Novel Steering Angle Calculation
for Autonomous Vehicles," 2018 Second IEEE International Conference on Robotic Computing (IRC),
Laguna Hills, CA, 2018, pp. 143-146, doi: 10.1109/IRC.2018.00029. https://github.com/lhzlhz/PilotNet