State Farm Distracted Driver Detection

Goal: Predict the likelihood of what the driver is doing in each picture

Final Report

Dataset:

State Farm Distracted Driver Detection Data

The 10 classes to predict are:

c0: safe driving
c1: texting - right
c2: talking on the phone - right
c3: texting - left
c4: talking on the phone - left
c5: operating the radio
c6: drinking
c7: reaching behind
c8: hair and makeup
c9: talking to passenger

Removed metadata such as creation dates. The train and test data are split on the drivers, such that one driver can only appear on either train or test set.

Documentation

Capstone_Project.pdf : final report of the capstone project

classifier/

classifier.py : run python classifier.py to create classifiers and save score and submission

data_analysis/

analyze_data.py : run python analyze_data.py to obtain stats about the training data
hog_visualizer.py : run python hog_visualizer.py to create and save hog descriptors for a random subject
surf_matching.py : run python surf_matching.py to obtain surf descriptors and match key points for two images for each class
plot_results.py : run python plot_results.py create html that shows a visualization of the classifiers performance according to feature set
output/ :
hog_images.png : hog descriptor example for report
surf_group1.png : surf descriptor example for report
surf_group2.png : surf descriptor example for report

imgs/

test/ : test images that needs prediction
train/ : 10 folders of labeled images
driver_imgs_list.csv : csv that contains image name, label, subject id

objects/

test/ : pickled objects for test images
train/ : pickled objects for train images

submissions/

base_submission.csv : predictions for using haralicks features only and Logistic Regression
combined_all_submission.csv : predictions for using haralicks, surf and lbps features and Logistic Regression
combined_submission.csv : predictions for using haralicks features and lbps features and Logistic Regression
lbps_submission.csv : predictions for using lbps features only and Logistic Regression
surf_submission.csv : predictions for using surf features only and Logistic Regression
final_model_results.html : visualization of Logistic Regression performance based on feature set
results_LR.image.txt : Score and best parameters for each feature set combination for model Logistic Regression
results_RF.image.txt : Score and best parameters for each feature set combination for model one vs rest with Random Forests

License

mwakaba2/Computer-Vision-Distracted-Driver-Project

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