This is SOID v0.1. We are adding more vocabularies and examples for the next release.

Overview

  • There are five classes.
    NameLabel =  {construction: 0, crowd: 1, pothole: 2, person-bike: 3, person-pet: 4}
    
  • Images are 96x96 pixels, color.
  • 50K images, each class has 10K images.
  • Images are acquired from taking pictures from the sidewalk and from the google image search.

Sidewalk Obstacle Image Dataset

Download

The sidewalk obstacle data v0.1 is release under MIT license and is available to download here

How to load in python

The data file is available in python pkl format.

import pickle
def load_file(filename):
    '''Load the sidewalk obstacle image data'''
    with open(filename, 'rb') as f:
        datadict= pickle.load(f)
        (X_train,Y_train),(X_valid,Y_valid),(X_test,Y_test) = datadict
    	return (X_train, Y_train),(X_valid,Y_valid),(X_test,Y_test)


# load training, validation and test set 
train,valid,test = load_file('sidewalk_rgb_all.pkl')

# separate the features and label 
X_train,y_train = train
X_valid,y_valid = valid
X_test, y_test  = test

Reference

  • Please cite the following reference in papers using theis dataset:
@inproceedings{sidewalk_ijcnn_2017,
author = {Faruk Ahmed and Mohammed Yeasin},
title = {Optimization and evaluation of deep architectures for ambient awareness
 on a sidewalk},
booktitle = {The International Joint Conference on Neural Networks},
year = {2017},
month = {May},
}
  • Please use http://cvpia.memphis.edu/sidewalk-obstacle-image-dataset/ as the URL when necessary

Contact

Send questions to Faruk Ahmed: mfahmed@memphis.edu

The MIT License (MIT)

Copyright (c) 2013 Damian Krzeminski

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