Deep Learning using Python/C++/OpenCV

Basics

• Deep Learning Basics
• Components of Autonomous Driving System
• Datasets
• Train your own object detector with Faster-RCNN & PyTorch

Computer Vision and Deep Learning

P1 - Detecting Lane Lines

• Basic: Detected highway lane lines on a video stream. Used OpencV image analysis techniques to identify lines, including Hough Transforms and Canny edge detection.
• Keywords: Computer Vision, OpenCV

P2 - Traffic Sign Classification

• Summary: Built and trained a support vector machines (SVM) to classify traffic signs, using dlib. Google Street View images can be used to train the detectors. 25~40 images are sufficient to train a good detector.
• Keywords: Computer Vision, Machine Learning

P3 - Object Detection with OpenCV

• Summary: The provided API (for C++ and Python) is very easy to use, just load the network and run it. Multiple inputs/outputs are supported. Here are the examples: https://github.com/opencv/opencv/tree/master/samples/dnn.

P4 - Vehicle Detection and Tracking

• Summary: Created a vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM). Implemented the same pipeline using a deep network to perform detection. Optimized and evaluated the model on video data from a automotive camera taken during highway driving.
• Keywords: Computer Vision, Deep Learning, OpenCV

P5 - Road Segmentation

• Summary: Implement the road segmentation using a fully-convolutional network.
• Keywords: Deep Learning, Semantic Segmentation

References

• https://github.com/spmallick/learnopencv
• https://github.com/ndrplz/self-driving-car