sumitasok / carnd-vehicle-detection Goto Github PK

Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector. Note: for those first two steps don't forget to normalize your features and randomize a selection for training and testing.

• png image read using matlib was scaled to 0-255
• the data provided was split int 80 % training data and 20 % testing data
• got an accuracy of 96-97 %

Implement a sliding-window technique and use your trained classifier to search for vehicles in images.

created boxes with 2 different widows and different overlaps. 

Run your pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles. Estimate a bounding box for vehicles detected.

[](http://www.youtube.com/watch?v= hha0NsYXS5c "Video Title")