Basic Usage#

With’s Conda environment activated (activate it with conda activate idtrackerai), run the command:


to launch the Segmentation app, a graphical application designed to help you define the correct input parameters for your videos. There you can select the desired video(s) to track, set the basic parameters and start the tracking process.

Terminal usage#

From the Segmentation app, you can start tracking directly or you can save the specified parameters in a .toml file like this one:

name = 'example'
video_paths = ['/home/user/idtrackerai/video_A.avi']
intensity_ths = [0, 155]
area_ths = [100.0, inf]
tracking_intervals = ""
number_of_animals = 8
use_bkg = false
check_segmentation = false
resolution_reduction = 1.0
track_wo_identities = false
roi_list = ['+ Polygon [[138.0, 50.1], [992.9, 62.1], [996.9, 878.9]]']
exclusive_roi = false

This file contains the full configuration defined in the Segmentation app and it can be loaded anytime with

idtrackerai --load example.toml

to recover the app as you left it, or with

idtrackerai --load example.toml --track

to load the parameters from example.toml and start the tracking process without any prior graphical interface. This feature allows the control of in remote via ssh and the capability to write custom scripts to run sequences of tracking sessions.

More advanced parameters can be used to extend’s capabilities. These can be loaded from a .toml file by using the same --load argument (see the details of these Advanced parameters below in this page).

Finally, any additional parameter can be passed in the command line as --PARAMETER VALUE.

An example of an advanced command could be:

idtrackerai --load my_basic_settings.toml example.toml --track_wo_identities true --number_of_animals 15 --track


Parameters files defined in --load are processed in increasing order of priority, this means that the last ones override the ones before them. In the example above, declarations in example.toml would override the ones in my_basic_settings.toml (in case they are about the same parameter). Any command line declaration overrides all files from --load.


In the case of running in remote (where the session parameters may have been created in another computer), it could be helpful to override, for example, the video paths from example.toml:

idtrackerai --load example.toml --video_paths path/in/remote/computer.avi --track

Tracking log#

During tracking, will communicate with the user through the log. This log will be displayed live in the terminal (Anaconda prompt on Windows) and written in the idtrackerai.log file in the current working directory. Users should keep an eye to the log, checking its status and warnings.

Idtracker may ask you to decide what to do if Protocol 3 is reached, see Tracking checks.

When a critical error occurs, the log contains all the information to solve it. Read the last lines of it to know more about what went wrong or send it to us so that we can help you.

Advanced parameters#

Besides the basic parameters from the segmentation app (the ones in example.toml), more advanced parameters can be used.


  • All parameters names are case insensitive.

  • Define path variables using 'single quotes' instead of "double ones" in the toml files to avoid backslashes (\) to trigger special characters (see TOML documentation to know more)

  • The value '' in a toml file is loaded as a Python’s None.


  • OUTPUT_DIR. Sets the directory path where the output session folder will be saved, by default it is the input video directory.

    output_dir = ''
  • CONVERT_TRAJECTORIES_TO_CSV_AND_JSON. The output trajectories are saved in a .npy file format (see Trajectory files). This type of files are not human readable and can only be loaded with Python. If true, a copy of these files in .csv and .json formats are generated when running Since version 5.1.7, the default is True:

    convert_trajectories_to_csv_and_json = true
  • BOUNDING_BOX_IMAGES_IN_RAM If true, bounding box images, a middle step to generate the identification images, will be kept in RAM until no longer needed. Else they are saved in disk and loaded when needed. Set this to true when working with very slow disks (HDD) to speed up segmentation.

    bounding_box_images_in_ram = false
  • ADD_TIME_COLUMN_TO_CSV. If true and also convert_trajectories_to_csv_and_json = true a time column (in seconds) is added to the csv trajectory files, the default is false:

    add_time_column_to_csv = false
  • DATA_POLICY. The tracking algorithms generate lots of data saved in the session folder and some can be safely removed. Select one of the following policies to clean the output data when the tracking succeeds (ordered from less to more data expensive).

    • "trajectories": only the trajectories will be saved, the rest of the data will be deleted.

    • "validation": only the data necessary to validate the trajectories will be saved, the rest will be deleted.

    • "knowledge_transfer": the data necessary to perform transfer learning or identity transfer will be kept.

    • "": the data necessary to perform the matching of identities using will be kept. This option is the optimal one, removing only no longer needed data.

    • "all": all the data generated during the tracking process is conserved (the default).

    data_policy = ""


    data_policy = "" is the optimal choice. It will delete only the data not going to be used in any case.

Background subtraction#

When subtracting background, a stack of video frames is generated to later compute the background estimation using some statist method

  • BACKGROUND_SUBTRACTION_STAT. Sets the statistic method to compute the background, choices are "median" (default), "mean", "max" (for dark animals on bright backgrounds) and "min" (for bright animals on dark backgrounds).

    background_subtraction_stat = "median"
  • NUMBER_OF_FRAMES_FOR_BACKGROUND. Sets the number of frames used to compute the background. These are equally spaced along the tracking intervals. More frames means more accuracy but also more computing time and RAM usage (only when computing the background).

    number_of_frames_for_background = 50

Tracking checks#

  • CHECK_SEGMENTATION. The presence in the video of frames with more blobs than animals means a bad segmentation with non-animal blobs detected. is not built to deal with non-animal blobs (noise blobs), these can contaminate the algorithms making the identification harder. To ensure a proper segmentation, set this to true and will abort the tracking session if a bad segmentation is detected.

    check_segmentation = false


    This parameter appears on the segmentation app as Stop tracking if #blobs > #animals.

  • PROTOCOL3_ACTION. Protocol 3 is called when both protocols 1 and 2 fail identifying animals. This protocol is very time consuming and, in most cases, it can be avoided by redefining the segmentation parameters. With this parameter you can choose the action should take when facing Protocol 3. Choices are "ask" (ask the user to decide what to do by answering through the terminal), "continue" and "abort". Default is "ask".

    protocol3_action = "ask"

Parallel processing#

Some parts in are parallelized (segmentation and identification images creation). This is done by slicing the video in different chunks and giving them to a group of independent workers to process.

  • NUMBER_OF_PARALLEL_WORKERS. Sets the number of workers used in the parallel parts. A negative value means using as many workers as the total number of CPUs minus the specified value. Zero value means running half of the total number of CPUs in the system or 8 if the system has more than 16 cores (using more than 8 cores doesn’t provide any significant speed up). One means no multiprocessing at all. The default value is 0.

    number_of_parallel_workers = 0


    During segmentation, every worker can use up to 4GB of memory, using too many workers might fill your RAM memory very fast. Computers with a large number of CPU cores (>10) should be monitored and the number of parallel workers should be adjusted accordingly.

  • FRAMES_PER_EPISODE. Sets the size of the video chunks (episodes). Lass frames per episode means more parallel chunks. By default:

    frames_per_episode = 500

Knowledge and identity transfer#

You can use the knowledge acquired by a previously trained convolutional neural network as a starting point for the training and identification protocol. This can be useful to speed up the identification when the videos are very similar (same light conditions, same distance from camera to arena, same type and size of animals).

  • KNOWLEDGE_TRANSFER_FOLDER.: Set the path to a session or accumulation folder from a previous tracked video. For example "/home/username/session_test" or "/home/username/session_test/accumulation_0". By default, every identification protocol starts from scratch.

    knowledge_transfer_folder = ''
  • IDENTITY_TRANSFER.: If the animals being tracked are the same as the ones from the knowledge_transfer session, there is the possibility to perform identity transfer. If so, will use the network from the knowledge_transfer* session to assign the identities of the current session. In our experience, for this to work the video conditions need to be almost identical to the previous video. The default:

    identity_transfer = false
  • ID_IMAGE_SIZE. By default, identification images size are optimized for current animal sizes in each video. Override this behavior by defining this parameter to an integer (the size in pixels of the side of the square image). Useful to make sure two sessions have the same identification image size (used in

    id_image_size = ''


There are alternative ways of transferring identities between tracking sessions. Check our tool, it requires the identification image size to be equal for all the sessions.

Basic parameters#

The assignment of any basic parameter (like the ones in example.toml) in the settings file acts as a default. For example, if you always track videos with 8 animals, you can set number_of_animals = 8 in you settings file and, when running idtrackerai --load settings.toml, the segmentation app will run with 8 animals as default.

Advanced hyper-parameters#


These parameters change the way the CNN is trained, use with care.

  • THRESHOLD_EARLY_STOP_ACCUMULATION.: Ratio of accumulated images needed to early stop the accumulation process. By default:

    threshold_early_stop_accumulation = 0.999
  • THRESHOLD_ACCEPTABLE_ACCUMULATION.: Minimum ratio of accumulated images that an accumulation process needs to have at the end to be accepted as successful. By default:

    threshold_acceptable_accumulation = 0.9
  • MAXIMAL_IMAGES_PER_ANIMAL.: Maximum number of images per animal that will be used to train the CNN in each accumulation step. By default:

    maximal_images_per_animal = 3000

File example#

An example settings file with all parameters as default (no effect) is

example settings.toml#
# Segmentation app defaults
name = ''
video_paths = ''
intensity_ths = [0, 130]
area_ths = [50.0, inf]
tracking_intervals = ""
number_of_animals = 0
use_bkg = false
check_segmentation = false
resolution_reduction = 1.0
track_wo_identities = false
roi_list = []

# Output
output_dir = ''
convert_trajectories_to_csv_and_json = true
bounding_box_images_on_ram = false
add_time_column_to_csv = false
data_policy = ''

# Background subtraction
background_subtraction_stat = 'median'
number_of_frames_for_background = 50

# Parallel processing
number_of_parallel_workers = 0
frames_per_episode = 500

# Knowledge and identity transfer
knowledge_transfer_folder = ''
identity_transfer = false
id_image_size = ''

# Tracking checks
protocol3_action = "ask"

# Advanced hyper-parameters
threshold_early_stop_accumulation = 0.999
threshold_acceptable_accumulation = 0.9
maximal_images_per_animal = 3000

Complete list of parameters#

idtrackerai -h prints the list of all possible command line arguments:

-h, --help

show this help message and exit

--load <path …>

A list of .toml files to load session parameters in increasing priority order.

--settings <path>

DEPRECATED, use –load with multiple files instead.


Track the video without launching the GUI

--tracking_intervals <pair_of_ints …>

Tracking intervals in frames. Examples: “0,100”, “[0,100]”, “[0,100] [150,200] …”. If none, the whole video is tracked (default: None).

--identity_transfer <bool>

If true, identities from knowledge transfer folder are transferred (default: False).

--intensity_ths <float float>

Blob’s intensity thresholds. When using background subtraction, the background difference threshold is the second value of these intensity thresholds (default: None).

--area_ths <float float>

Blob’s areas thresholds (default: None).

--number_of_animals <int>

Number of different animals that appear in the video (default: 0).

--output_dir <path>

Output directory where session folder will be saved to, default is video paths parent directory (default: None).

--resolution_reduction <float>

Video resolution reduction ratio (default: 1.0).

--check_segmentation <bool>

Check all frames have less or equal number of blobs than animals (default: False).

--roi_list <str …>

List of polygons defining the Region Of Interest (default: None).

--use_bkg <bool>

Compute and extract background to improve blob identification (default: False).

--video_paths <path …>

List of paths to the video files to track (default: []).

--session <str>

DEPRECATED, use “–name”.

--name <str>

Name of the session (default: name of the video files).

--track_wo_identities <bool>

Track the video ignoring identities (without AI) (default: False).

--convert_trajectories_to_csv_and_json <bool>

If true, trajectories files are gonna be copied to .csv and .json files (default: True).

--add_time_column_to_csv <bool>

If true, adds a time column (in seconds) to csv trajectory files (default: False).

--frames_per_episode <int>

Maximum number of frames for each video episode (used to parallelize some processes) (default: 500).

--knowledge_transfer_folder <path>

Path to the session to transfer knowledge from (default: None).

--background_subtraction_stat <str>

Statistical method to compute the background (choices: median, mean, max, min) (default: median).

--protocol3_action <str>

Choose what to do when protocol 1 and 2 fail and protocol 3 is going to start (choices: ask, abort, continue) (default: ask).

--number_of_frames_for_background <int>

Number of frames used to compute the background (default: 50).

--number_of_parallel_workers <int>

Maximum number of jobs to parallelize segmentation and identification image creation. A negative value means using the number of CPUs in the system minus the specified value. Zero means using half of the number of CPUs in the system (limited to 8). One means no multiprocessing at all (default: 0).

--data_policy <str>

Type of data policy indicating the data in the session folder not to beerased when successfully finished a tracking (choices: trajectories, validation, knowledge_transfer,, all) (default:

--id_image_size <int>

The size of the identification images used in the tracking (default: []).

--exclusive_rois <bool>

(experimental feature) Treat each separate ROI as closed identities groups (default: False).

--threshold_early_stop_accumulation <float>

(advanced hyperparameter) Ratio of accumulated images needed to early stopping the accumulation process (default: 0.999).

--threshold_acceptable_accumulation <float>

(advanced hyperparameter) Minimum ratio of accumulated images that an accumulation process needs to be accepted as successful (default: 0.9).

--maximal_images_per_animal <int>

(advanced hyperparameter) Maximum number of images per animal that will be used to train the CNN in each accumulation step (default: 3000).

--bounding_box_images_in_ram <bool>

If true, bounding box images, a middle step to generate the identification images, will be kept in RAM until no longer needed. Else, they are saved in disk and loaded when needed (default: False).