Urban Safety Perception Bogota 2018

Image set [imgset1-11.zip]

5505 street images of the Chapinero locality

Indexed actual vote image pair annotations

descriptorIndexer_Jul_0518.txt
Indexing starts at 1 not at 0 !!
18959 annotations

Visual survey published image name list

imgnames.txt

Visual survey published image feature vector files

cielabA, gist and hog features.zip

1. cielabA2.txt
2. cielabB2.txt
3. cielabL2.txt
4. gistSet.txt
5. hogSet.txt

VGG19_features.zip

1. VGG19Chapinero_Ftrs.csv

notebooks

transfer4uspVGG16FtrExtr.ipynb

Used for image VGG19 based feature extraction

randomVoteSchemeIII.ipynb and randomVoteSchemeIV.ipynb

Used to generate synthetic vote image pairs.

transfer4uspVGG16SoftMaxNonEQU.ipynb

Training notebook

transfer4uspVGG16NonEQUVerify_Jul_0518.zip

Tensor Flow model

TrueskillImgScoreShmIII.ipynb

Top 40 image rating visualization

True Skill based predictors

VGG19ChapineroTSkillPredictor.py

VGG19MartiresTSkillPredictor.py

VGG19UsaquenTSkillPredictor.py

OpenMPI C Code for SVM parameter grid exploration

This works along with NFS and MPI multi core machine cluster

mpi_svmprtrs.c

Papers

• Acosta, S., Camargo, J. City safety perception model based on visual content of street images. IEEE IV International Smart Cities Conference (ISC2), 2018. pdf

• Acosta, S., Camargo, J. Predicting city safety perception based on visual image content. 23rd Iberoamerican Congress on Pattern Recognition (CIARP), 2018.

• Acosta S.F., Camargo J.E. (2019) Predicting City Safety Perception Based on Visual Image Content. In: Vera-Rodriguez R., Fierrez J., Morales A. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2018. Lecture Notes in Computer Science, vol 11401. Springer, Cham.