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https://github.com/cvondrick/vatic

Efficiently Scaling Up Video Annotation with Crowdsourced Marketplaces. IJCV 2012
https://github.com/cvondrick/vatic

computer-vision video video-annotation

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Efficiently Scaling Up Video Annotation with Crowdsourced Marketplaces. IJCV 2012

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README 

        
# Please Note



Intel has created an excellent annotation tool with the latest technologies. https://github.com/opencv/cvat 



The project below is archived, and no further updates are expected.



2009 to 2020



# VATIC - Video Annotation Tool from Irvine, California



VATIC is an online video annotation tool for computer vision research that

crowdsources work to Amazon's Mechanical Turk. Our tool makes it easy to build

massive, affordable video data sets. 






# INSTALLATION 



Note: VATIC has only been tested on Ubuntu with Apache 2.2 HTTP server and a

MySQL server. This document will describe installation on this platform,

however it should work any operating system and with any server.



## Download



You can download and extract VATIC from our website. Note: do NOT run the 

installer as root. 



    $ wget http://mit.edu/vondrick/vatic/vatic-install.sh

    $ chmod +x vatic-install.sh

    $ ./vatic-install.sh

    $ cd vatic



## HTTP Server Configuration 



Open the Apache configuration file. On Ubuntu, this file is located at:



    /etc/apache2/sites-enabled/000-default



If you do not use Apache on this computer for any other purpose, replace the

contents of the file with:



    WSGIDaemonProcess www-data

    WSGIProcessGroup www-data



    

        ServerName vatic.domain.edu

        DocumentRoot /path/to/vatic/public



        WSGIScriptAlias /server /path/to/vatic/server.py

        CustomLog /var/log/apache2/access.log combined

    



updating ServerName with your domain name, DocumentRoot with the path to

the public directory in VATIC, and WSGIScriptAlias to VATIC's server.py file.



If you do use Apache for other purposes, you will have to setup a new virtual

host with the correct document root and script alias, as shown above.



Make sure you have the mod_headers module enabled:



    $ sudo cp /etc/apache2/mods-available/headers.load /etc/apache2/mods-enabled



After making these changes, restart Apache:



    $ sudo apache2ctl graceful



## SQL Server Configuration 



We recommend creating a separate database specifically for VATIC:



    $ mysql -u root

    mysql> create database vatic;



The next section will automatically create the necessary tables.



## Setup



Inside the vatic directory, copy config.py-example to config.py:



    $ cp config.py-example config.py



Then open config.py and make changes to the following variables in order to

configure VATIC:



    signature       Amazon Mechanical Turk AWS signature (secret access key)

    accesskey       Amazon Mechanical Turk AWS access key (access key ID)

    sandbox         If true, put into Mturk sandbox mode. For debugging.

    localhost       The local HTTP address: http://vatic.domain.edu/ so it

                    matches the ServerName in Apache.

    database        Database connection string: for example,

                    mysql://user:pass@localhost/vatic

    geolocation     API key from ipinfodb.com for geolocation services



If you do not plan on using VATIC on Mechcanical Turk (offlien mode only), you

can leave the signature and accesskey empty.



After saving results, you can then initialize the database:



    $ turkic setup --database



Note: if you want to reset the database, you can do this with:



    $ turkic setup --database --reset



which will require confirmation to reset in order to prevent data loss.



Finally, you must also allow VATIC to access turkic, a major dependency:



    $ turkic setup --public-symlink



# ANNOTATION 



Before you continue, you should verify that the installation was correct. You

can verify this with:



    $ turkic status --verify



If you receive any error messages, it means the installation was not complete

and you should review the previous section. Note: If you do not plan on

using Mechanical Turk, you can safely ignore any errors caused by Mechanical

Turk.



## Frame Extraction 



Our system requires that videos are extracted into JPEG frames. Our tool can 

do this automatically for you:



    $ mkdir /path/to/output/directory

    $ turkic extract /path/to/video.mp4 /path/to/output/directory



By default, our tool will resize the frames to fit within a 720x480 rectangle.

We believe this resolution is ideal for online video viewing. You can change 

resolution with options:



    $ turkic extract /path/to/video.mp4 /path/to/output/directory

      --width 1000 --height 1000



or



    $ turkic extract /path/to/video.mp4 /path/to/output/directory

      --no-resize



The tool will maintain aspect ratio in all cases.



Alternatively, if you have already extracted frames, you can use the

formatframes command to format the video into a format that VATIC understands:



    $ turkic formatframes /path/to/frames/ /path/to/output/directory



The above command will read all the images in /path/to/frames and create

hard links (soft copy) in /path/to/output/directory.



## Importing a Video



After extracting frames, the video can be imported into our tool for 

annotation. The general syntax for this operation is:



    $ turkic load identifier /path/to/output/directory Label1 Label2 LabelN



where identifier is a unique string that you will use to refer to this video,

/path/to/output/directory is the directory of frames, and LabelX are class

labels that you want annotated (e.g., Person, Car, Bicycle). You can have as

many class labels as you wish, but you must have at least one.



When a video is imported, it is broken into small segments typically of only a

few seconds. When all the segments are annotated, the annotations are merged

across segments because each segment overlaps another by a small margin.



The above command specifies all of the required options, but there are many

options available as well. We recommend using these options.



    MTurk Options

        --title         The title that MTurk workers see

        --description   The description that MTurk workers see

        --duration      Time in seconds that a worker has to complete the task

        --lifetime      Time in seconds that the task is online

        --keywords      Keywords that MTurk workers can search on

        --offline       Disable MTurk and use for self annotation only



    Compensation Options

        --cost                  The price advertised to MTurk workers

        --per-object-bonus      A bonus in dollars paid for each object

        --completion-bonus      A bonus in dollars paid for completing the task



    Qualification Options

        --min-approved-percent  Minimum percent of tasks the worker must have

                                approved before they can work for you

        --min-approved-amount   Minimum number of tasks that the worker must 

                                have completed before they can work for you



    Video Options

        --length        The length of each segment for this video in frames

        --overlap       The overlap between segments in frames

        --use-frames    When splitting into segments, only the frame intervals

                        specified in this file. Each line should contain a

                        start frame, followed by a space, then the stop frame.

                        Frames outside the intervals in this file will be

                        ignored.

        --skip          If specified, request annotations only every N frames.

        --blow-radius   When a user marks an annotation, blow away all other

                        annotations within this many frames. If you want to

                        allow the user to make fine-grained annotations, set

                        this number to a small integer, or 0 to disable. By

                        default, this is 5, which we recommend.



You can also specify temporal attributes that each object label can take on.

For example, you may have a person object with attributes "walking", "running",

or "sitting". You can specify attributes the same way as labels, except you

prepend an ~ before the text, which bind the attribute to the previous label:



    $ turkic load identifier /path/to/output/directory Label1 ~Attr1A ~Attr1B

      Label2 ~Attr2A ~Attr2B ~Attr2C Label3 



In the above example, Label1 will have attributes Attr1A and Attr1B, Label2

will have attributes Attr2B, Attr2B, and Attr2C and Label3 will have no 

attributes. Specifying attributes is optional.



## Gold Standard Training 



It turns out that video annotation is extremely challenging and most MTurk

workers lack the necessary patience. For this reason, we recommend requiring

workers to pass a "gold standard" video. When a new worker visits the task,

they will be redirected to a video for which the annotations are already known.

In order to move on to the true annotations, the worker must correctly annotate

the gold standard video first. We have found that this approach significantly

improves the quality of the annotations.



To use this feature, import a video to be used as the gold standard:



    $ turkic load identifier-train /path/to/frames Label1 Label2 LabelN

      --for-training --for-training-start 0 --for-training-stop 500

      --for-training-overlap 0.5 --for-training-tolerance 0.1

      --for-training-mistakes 1



You can also use any of the options described above. Explanations for the new

options are as follows:



    --for-training              Specifies that this video is gold standard

    --for-training-start        Specifies the first frame to use

    --for-training-stop         Specifies the last frame to use

    --for-training-overlap      Percent overlap that worker's boxes must match 

    --for-training-tolerance    Percent that annotations must agree temporally

    --for-training-mistakes     The number of completely wrong annotations 

                                allowed. We recommend setting this to a small,

                                nonzero integer.



After running the above command, it will provide you with an URL for you to

input the ground truth annotation. You must make this ground truth annotation

as careful as possible, as it will be used to evaluate future workers.



You can now specify that a video should use a gold standard video:



    $ turkic load identifier /path/to/output/directory Label1 Label2 LabelN

      --train-with identifier-train



When a not-yet-seen worker visits this video, they will now be redirected to

to the training video and be required to pass the evaluation test first.



## Publishing Tasks 



When you are ready for the MTurk workers to annotate, you must publish the 

tasks, which will allow workers to start annotating:

    

    $ turkic publish



You can limit the number of tasks that are published:



    $ turkic publish --limit 100



Running above command repeatedly will launch tasks in batches of 100. You can

also disable all pending tasks:

    

    $ turkic publish --disable



which will "unpublish" tasks that have not yet been completed.



If you have videos that are offline only, you can see their access URLs with

the command:



    $ turkic publish --offline



Note: for the above command to work, you must have loaded the video with the

--offline parameter as well: 



    $ turkic load identifier /path/to/frames Person --offline



## Checking the Status 



You can check the status of the video annotation server with the command:



    $ turkic status



This will list various statistics about the server, such as number of jobs

published and how many are completed. You can get even more statistics by

requesting additional information from Amazon:



    $ turkic status --turk



which will output how much money is left in your account, among other

statistics.



When all the videos are annotated, the last line will read:



    Server is offline.



## Retrieving Annotations



You can get all the annotations for a video with the command:



    $ turkic dump identifier -o output.txt



which will write the file "output.txt" where each line contains one

annotation. Each line contains 10+ columns, separated by spaces. The

definition of these columns are:



    1   Track ID. All rows with the same ID belong to the same path.

    2   xmin. The top left x-coordinate of the bounding box.

    3   ymin. The top left y-coordinate of the bounding box.

    4   xmax. The bottom right x-coordinate of the bounding box.

    5   ymax. The bottom right y-coordinate of the bounding box.

    6   frame. The frame that this annotation represents.

    7   lost. If 1, the annotation is outside of the view screen.

    8   occluded. If 1, the annotation is occluded.

    9   generated. If 1, the annotation was automatically interpolated.

    10  label. The label for this annotation, enclosed in quotation marks.

    11+ attributes. Each column after this is an attribute.



By default, the above command will not attempt to merge annotations across

shot segments. You can request merging with the command:



    $ turkic dump identifier -o output.txt --merge --merge-threshold 0.5



The --merge-threshold option is optional, but it is a number between 0 and 1

that represents much the paths must agree in order to merge. 1 specifies a

perfect match and 0 specifies no match. In practice, 0.5 is sufficient. Merging

is done using the Hungarian algorithm.



You can also scale annotations by a factor, which is useful for when the

videos have been downsampled:



    $ turkic dump identifier -o output.txt -s 2.8



or force it to fit within a max dimension:



    $ turkic dump identifier -o output.txt --dimensions 400x200



or force it to fit within the dimensions of the original video:



    $ turkic dump identifier -o output.txt --original-video /path/to/video.mp4



The command can also output to many different formats. Available formats are:



    --xml       Use XML

    --json      Use JSON

    --matlab    Use MATLAB

    --pickle    Use Python's Pickle

    --labelme   Use LabelMe video's XML format

    --pascal    Use PASCAL VOC format, treating each frame as an image



The specifications for these formats should be self explanatory.



## Visualizing Videos 



You can preview the annotations by visualizing the results:



    $ turkic visualize identifier /tmp --merge



which will output frames to /tmp with the bounding boxes with the file name

as the frame number. The visualization will contain some meta information

that can help you identify bad workers. You can remove this meta information

with the option:



    $ turkic visualize identifer /tmp --merge --no-augment



If you want to make a video of the visualization (e.g., with ffmpeg), it is

useful to renumber the frames so that they start counting at 0 and do not

have any gaps:



    $ turkic visualize identifier /tmp --merge --renumber



If you wish to display the class label and their attributes next to the box,

specify the --labels option:



    $ turkic visualize identifier /tmp --labels



## Compensating Workers 



When you are ready, you can compensate workers:



    $ turkic compensate --default accept



which will pay all workers for all outstanding tasks. We strongly recommend

paying all workers regardless of their quality. You should attempt to pay

workers at least once per day.



## Finding Jobs 



If you have found a small mistake in a video and want to make

the correction yourself, you can start an annotation session initialized with

the MTurk workers annotations:



    $ turkic find --id identifier

    $ turkic find --id identifier --frame frame



where identifier is the identifier for the video and frame is the frame number

that the error occurs. In most cases, this command will return one URL for you

to make the corrections. If it outputs two URLs, it means the frame number

occurs in two overlapping segments, and so you may have to make changes to both

of the segments. You can also omit the frame argument, in which case it will

output all URLs for that video.



If you want to find the HIT id, assignment ID, or worker ID for a particular

video, specify the --ids parameter to the vet command:



    $ turkic find --id identifer --ids

    $ turkic find --id identifer --frame frame --ids



will print a list of all the IDs for the video. If the corresponding segment

has been published and completed, it will list three strings: the HIT ID,

assignment ID, and the worker ID. If the job has been published but not

finished, it will just list the HIT ID. If the job has not yet been published,

it prints "(not published)".



Additionally, if you want to find the job that corresponds to a particular

HIT ID, you can use the find command:



    $ turkic find --hitid HITID



## Quality Control 



The gold standard does a "pretty good" job of weeding out bad workers.

Nonetheless, there will always be bad workers that we must identify and

invalidate. Our tool provides a method to sample the annotations provided by

workers, which you can then manually verify for correctness:



    $ turkic sample /tmp



which by default will pick 3 random videos that the worker has completed, and

pick 4 random frames from each of those videos, and write visualiations to a

file in /tmp. You can tweak the number of videos and the number of frames with

the options:



    $ turkic sample /tmp --number 3 --frames 4



Moreover, you can only look at work from a certain date:



    $ turkic sample /tmp --since "yesterday"



The filename will follow the format of WORKERID-JOBID.jpg. Once you have

identified a mallicious worker, you can block them, invalidate ALL of their

work, and respawn their jobs with the command:



    $ turkic invalidate workerid



The options are also available:



    --no-block      invalidate and respawn, but don't block

    --no-publish    block and invalidate, but don't respawn



You can also invalidate and respawn individual jobs with the command:



    $ turkic invalidate --hit hitid



## Listing all Videos 



You can retrieve a list of all videos in the system with:



    $ turkic list



If you want just the videos that have been published:



    $ turkic list --published



If you want just the videos that have been worked on:



    $ turkic list --completed



If you instead want the videos that are used for gold standard:



    $ turkic list --training



Finally, if you just want to count how many videos are in the system, use the

--count option, in combination with any of the above:



    $ turkic list --count

    $ turkic list --published --count



If you want statistics about each video, then give the --stats option:



    $ turkic list --stats



## Managing Workers 



You can list all known workers with the command:



    $ turkic workers



which will dump every worker with the number of jobs they have completed. You

can also use this command to block and unblock workers:



    $ turkic workers --block workerid

    $ turkic workers --unblock workerid



You can also search for workers by the first few letters of their ID:



    $ turkic workers --search A3M



## Deleting a Video 



You can delete a video at any time with:



    $ turkic delete identifier



If the video has already been annotated (even partially), this command will 

warn you and abort. You can force deletion with:



    $ turkic delete identifier --force



which will REMOVE ALL DATA AND CANNOT BE UNDONE.



# REFERENCES 



When using our system, please cite:



    Carl Vondrick, Donald Patterson, Deva Ramanan. "Efficiently Scaling Up

    Crowdsourced Video Annotation" International Journal of Computer Vision

    (IJCV). June 2012.



# FEEDBACK AND BUGS 



Please direct all comments and report all bugs to:



    Carl Vondrick

    [email protected]



Thanks for using our system!