Convolutional Neural Network for Pedestrian Classification

Script definitions

• createPatches.py Creates patches for input into CNN
• data.lua Data augmentation and train set class balancing
• train.lua Network training via Stocastic Gradient Descent (SGD)
• batchTrain.lua Network training with Batch Normalization and SGD
• imageParamid.lua Pedestrian detection with exhaustive search

Dependencies

1. Python 2.7+

   • numpy
   • pandas
   • scikit-learn
   • PIL
   • argparse

2. Torch7

   • npy4th
   • penlight
   • math
   • xlua
   • optim
   • image
   • lfs
   • nn / cunn

3. metrics.lua within the scripts/ directory

Training the model from start to finish

Training the model requires extracting the label patches and converting them to torch tensors createPatches.py. Then training the network with either a mini-batch SGD strategy train.lua or a mini-batch SGD strategy with Batch Normalization batchTrain.lua. Optionally, if there is an imbalanced between the two classes the data.lua script will balance the data via data augmentation. This last script can be run after extracting the patches.

1. Extract the patches

python createPatches.py

2. Balance the data (optional)

th data.lua

3. Train the model

th train.lua -table data/dev_data.table

If the data is not balanced the model will train with the source tensors. To train without the balanced set remove the argument -table data/dev_data.table from the above.