Authentically Distorted Surveillance Videos Dataset

Citation Author(s):
César Augusto
Ardila Franco
Santiago Nicolás
Bonilla Vergara
Hernán Darío
Benítez Restrepo
Senior Member
Roger Alfonso
Gómez Nieto
Graduate Student Member
Submitted by:
Cesar Ardila Franco
Last updated:
Thu, 05/07/2020 - 19:27
DOI:
10.21227/akyk-7b78
Data Format:
License:
0
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Abstract 

This Dataset contains "Pristine" and "Distorted" videos recorded in different places. The 

distortions with which the videos were recorded are: "Focus", "Exposure" and "Focus + Exposure". 

Those three with low (1), medium (2) and high (3) levels, forming a total of 10 conditions 

(including Pristine videos). In addition, distorted videos were exported in three different 

qualities according to the H.264 compression format used in the DIGIFORT software, which were: 

High Quality (HQ, H.264 at 100%), Medium Quality (MQ, H.264 at 75%) and Low Quality 

(LQ, H.264 at 50%).The recording of the videos was carried out in a semicontrolled space, 

with the appropriate environment conditions that facilitate the generation of authentically 

distorted videos. The "DIGIFORT" monitoring software is used to manage and record the videos. 

For the labeling of each video we use the "DarkLabel" labeling software. For the videos editing 

and processing the "MATLAB" computational tool is used. The dataset contains 160 "Pristine" 

videos, 1450 "Exposure" videos, 1409 "Focus" videos and 1450 "Focus + Exposure" videos for a 

total of 4476 videos. The recorded activities are: "Walking" (WL), "Leaving Package in a Public 

Place" (LPP), "Passing Out" (PO), "Person Pushing Person" (PPP), "Person Running" (PR), 

"Fighting in Group" (FG), "Robbery with Knife" (RK) and "Prowl" (PW). This Dataset is intended 

to evaluate "Visual Quality Assessment" (VQA) and "Visual Object Tracking" (VOT) algorithms.

 

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Instructions: 

  

0. This Dataset is intended to evaluate "Visual Quality Assessment" (VQA) and "Visual Object 

Tracking" (VOT) algorithms. It has 4476 videos with different distortions and their Bounding Box 

annotations ([x(x coordinate) y(y coordinate) w(width) h(height)]) for each frame. It also contains 

a MATLAB script which allows to generate the video sequences for VOT algorithms evaluation.

 

1. Move the "generateSequences.m" file to the "surveillanceVideosDataset" Folder.

 

2. Open the script and modify the next parameters according to your need:

 

%---------------------------------------------------------------%

                                                                              % 

%Sequence settings and images nomenclature   %

imagesType = '.jpg';                                              %

imgFolder = 'img';                                                 %  

gtName = 'groundtruth.txt';                                   %

imgNomenclature = ['%04d' imagesType];           %

                                                                             %

%--------------------------------------------------------------%

 

The last configuration will create a folder like this for each video:

 

0001SequenceExample (Folder)

- - img (Folder)

- - - - 0001.jpg (Image)

- - - - 0002.jpg (Image)

- - - - ....

- - - - ....

- - - - ....

- - - - 0451.jpg (Image)

- - groundtruth.txt (txt file: Bounding Box Annotations)

 

3. Press "Run" and wait until the sequences are built. The process can take a long time due to the 

number of videos. You will need 33 GB for the videos, 30 MB for the Bounding Box annotations and 230 

GB for the sequences (.jpg format).

 

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