1. Merge different car front view image data set and preprocess the image according to the phone camera quality.
2. Train a car front view detection model by Adaboostalgorithm using OpenCVlibrary.
3. Evaluate the car detector performance offline.
1. Training datasets:
MIT CBCL CAR DATABASE #1: 516 128x128 PPM format images
Caltech Cars 2001 (Rear): 526 images of rear view cars
Negative images: Google Street View images with no car 250x150 large images
Preprocess:
1. Crop the car regions and form 40x40 grayscale images. Remain as little background as possible in the positive samples to get better results.
2. Equalize color histogram in the whole image.
True Positive on training sets:
True Positive on test sets: INRIA Annotations for Graz-02 (IG02)
False Positive on test sets: INRIA Annotations for Graz-02 (IG02)
1. Evaluate the detection performance thoroughly
2. Modify the training dataset and retrain the car detector
3. Modify the training parameters for Adaboostalgorithm
4. Reduce false positive results
5. Collect car/noncarimages by Nexus One and evaluate the car detector on this test dataset
6. Port the car detection model to Nexus One and evaluate the accuracy and realtime performance