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Vehicle Detection Project

The goals / steps of this project are the following:

• Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier
• Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
• Estimate a bounding box for vehicles detected.
• Run this pipeline on a video stream

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Histogram of Oriented Gradients (HOG)

I defined the f_hog function to extract the hog feature (with the skimage.hog()) for every image. I set orientations = 11, pixels_per_cell = 16, and cells_per_block = 2 and used the yuv

Classifier

I processed the vehicle/non-vehicle dataset(feature_extraction and standardization); then I split in train and cross-validation set using train_test_split with stratification. After this I trained a support vector machine with rbf kernel obtaining a 0.991 of accuracy with 3-fold cv.

Sliding Window Search

I decided to search for different scales in the bottom part of the image: I tried, defining compute_windows and collect_windows, to compute the HOG feature for every windows, but it resulted to be a slow method; so in the find_subimages_boxes_features I computed one time the features sub-sampling for every windows. Here and example of the windows search space:

Bounding boxes

From the positive detections I created a heatmap and then thresholded that map to identify vehicle positions. I then used scipy.ndimage.measurements.label() to identify vehicles and I constructed bounding boxes to cover the area of each blob detected.

Result

I defined my pipeline: extract the feature vector, compute the prediction for vehicle detection, define the bounding boxes using; finally I obtained:

Video Implementation

For the video stream I implemented two method for false positive detection into the Detector() class: the first is the thresholding on the heatmap; the second is that, if in the actual frame we identify a different number of cars than in the last frame, we don't identify a box and we wait for a new detection in the next frame: if in the next frame the classifier identifies again the same number of vehicles I consider a true positive detection

Here's a link to my video result

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Discussion

My approach is not robust and there are problem when the vehicle is on the right of the image; I see some problem also when a new vehicle enters in the image. In general I have some false negative. And I could improve my classifier.