object-detector

Object Detector using HOG as descriptor and Linear SVM as classifier. | Video

Run the code

I have created a single python script that can be used to test the code. To test the code, run the lines below in your terminal.

git clone https://github.com/bikz05/object-detector.git
cd object-detector/bin
test-object-detector

The test-object-detector will download the UIUC Image Database for Car Detection and train a classifier to detect cars in an image. The SVM model files will be stored in data/models, so that they can be resused later on.

Configuration File

All the configurations are in the data/config/config.cfg configuration files. You can change it as per your need. Here is what the default configuration file looks like (which I have set for Car Detector)-

[hog]
min_wdw_sz: [100, 40]
step_size: [10, 10]
orientations: 9
pixels_per_cell: [8, 8]
cells_per_block: [3, 3]
visualize: False
normalize: True

[nms]
threshold: .3

[paths]
pos_feat_ph: ../data/features/pos
neg_feat_ph: ../data/features/neg
model_path: ../data/models/svm.model

About the modules

extract-features.py -- This module is used to extract HOG features of the training images.
train-classifier.py -- This module is used to train the classifier.
nms.py -- This module performs Non Maxima Suppression.
test-classifier.py -- This module is used to test the classifier using a test image.
config.py -- Imports the configuration variables from config.cfg.