Created a vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM). Optimized and evaluated the model on video data from a automotive camera taken during highway driving.
Version 2 uses the following parameters for feature extraction
feature_params = {
'color_model': 'yuv', # hls, hsv, yuv, ycrcb
'bounding_box_size': 64, # 64 pixels x 64 pixel image
'number_of_orientations': 11, # 6 - 12
'pixels_per_cell': 16, # 8, 16
'cells_per_block': 2, # 1, 2
'do_transform_sqrt': True
}
# [3 x 3 block positions] x [2 x 2 cells per block] x [11 orientations] x [3 channels] = 1,188 features
Version 1 uses the following parameters for feature extraction
feature_params = {
'color_model': 'hls', # hls, hsv, yuv, ycrcb
'bounding_box_size': 64, # 64 pixels x 64 pixel image
'number_of_orientations': 12, # 6 - 12
'pixels_per_cell': 8, # 8, 16
'cells_per_block': 2, # 1, 2
'do_transform_sqrt': True
}
# [7 x 7 block positions] x [2 x 2 cells per block] x [12 orientations] x [3 channels] = 7,056 features
To run any notebook properly, copy the jupyter notebooks from the /ipynb folder to the root directory.
This is so that each notebook sees relevant files, the most relevant files being the python classes.
- classifier_training.ipynb
- feature_sourcer_test.ipynb
- classifier_test.ipynb
- slider_test.ipynb
- heatmap_test.ipynb
- pipeline.ipynb
- featuresourcer.py
- binaryclassifier.py
- slider.py
- heatmap.py