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Distributed Acoustic Sensor System for Intelligent Transportation using Deep Learning

We present DAS sample and 1D/2D CNN for vehicle type and occupancy classification

  1. How to setup
    Step 1: Download and unzip preprocessed data in current directory: https://drive.google.com/file/d/1ore1g5sN8bUA7NvG5lvAGw9z6_LQlRuO/view?usp=drive_link

    or alternatively (not recommand)

    Download and unzip raw data (1) in current directory and run the following notebooks (2):

    Step 2: Install pandas, numpy, and tensorflow
    Step 3: Keep in mind how the names of datasets in this repository are related to the paper:

    name in paper pre-proccessed data in the repository dataset description
    RC-60-Mix 058_5p_to_1p_X.txt
    058_5p_to_1p_y.txt    		 Car 2 with 5, 4, 3, 2, 1 passengers
    AllCars-1p
    (= is now re-named to RC-60-1p)    		 026_X.txt
    026_y.txt    		 The file contains signals from
    Car 1, 2, 3, 4, 5 with 1 passengers (a driver)
    but only Car 2 data is used for this individual testing dataset
    RC-60-5p 058_5p_X.txt
    058_5p_y.txt    		 Car 2 with 5 passengers

  2. How to use this repository

    We try to reproduce the testing results from Table III and Table IIV in our paper (named the same as this repository) so we named jupyter notebooks the same as the experiments in Table III and Table IIV.

    For example, in 5 way - 1d.ipynb, we trained a 1dcnn to classifiy exact number of passengers (5 way: 5 classes each of them has different number of passenger from 1 to 5).

  3. Current reproducing results:

    5-way 5-way 2-way 2-way 2-way + 2-way +
    Model of CNN 1D 2D 1D 2D 1D 2D
    Train:Test 67:33 80:20 80:20 80:20 80:20 80:20
    RC-60-Mix 0.81 0.93 0.896 0.98 0.96
    Ind. AllCars-1p (RC-60-1p) 0.297 0.68 0.46 0.62 0.52
    Ind. RC-60-5p 0.099 0.28 0.247 0.52 0.53