Browser Based

Loopy is used through your web browser - there is no software to install. New features are constantly added to Loopy and are immediately available to all customers (no yearly upgrades required). Cloud and On-site versions are available.

Multipurpose

Loopy is suited for many types of video analysis tasks including: single or multiple tracking of animal positions in videos, behavioral coding and annotation, 3D reconstruction and calibration, and much more.

State-of-the-art

Loopy includes a number of state-of-the art techniques such as deep-learning based animal and pose detection in natural environments, tracking with identity, 3D tracking, and many more peer-reviewed and published algorithms.

Loopy Video Analysis and Tracking Features

Loopy includes a complete set of image processing, tracking and behavioral analysis tools in a single piece of software. Where others may charge you for multiple products and updates, with Loopy everything is in one place.

Please watch the video or read the description below where the major Loopy features are described. For examples of how to use Loopy's capabilities in youre research click on the 'See More Examples' in the respective sections below.


  • Tracking

    Track One or Multiple Subjects in Videos

    Loopy includes a number of general purpose algorithms for tracking the position of subjects in videos, including common laboratory model species, such as zebrafish, larvae, mice, and many more. Loopy includes barcode tracking functionality, and an easy to use tracking wizard, which helps to determine an optimal set of tracking parameters for your experiment.

    For even more challenging tracking problems, Loopy is the only all-in-one software which lets you train and then immediately use, your own custom deep learning based tracking solution.

  • Deep Learning

    Animal and Pose Tracking using Deep Learning

    Conventional image processing requires tedious hand tuning of parameters and strong visual differences between the animal and the background, usually resulting in poor solutions.

    Deep learning based models learn how to locate the animals when provided with a small fraction of the videos annotated, even working in data collected in the field. Loopy lets you train your own deep learning tracking solution for single and multiple animal and animal-pose tracking.

  • 3D

    3D Tracking of Animal Movement

    The real world is not two dimensional, and neither should your science be. Loopy includes tools for tracking and reconstructing the position of single or multiple animals or subject body parts - including using deep learning to detect and track previously manually impossible to track attributes.

  • Coding

    Scoring and Coding of Subject Behavior

    Loopy includes a powerful and efficient interface for coding focal or non-focal subject behavior from videos. One can code the occurrence and duration of events for multiple individuals. The tool also includes support for coding social paradigms where the directed interaction between individuals can be recorded. Loopy also includes plotting and analysis tools for understanding coded behavior.

  • Annotation

    Annotation of Subject Position or Attributes

    Loopy includes a web-based tool for annotating objects in videos. Collected annotation information, which can be bounding-box (rectangle) or object coordinates, can be used for training your own custom deep learning models, 3D tracking, or the raw data can downloaded for use in your own analyses.

  • Proc​essing

    Video Editing and General Video Processing Operations

    Loopy comes with a number of general purpose image processing algorithms including slow-motion and timelapse, video generation, audio waveform and spectrogram visualisation, OCR, color and white-balance correction and much more. These operations can be used to pre-process videos before tracking, or as stand-alone tools.

Video and Metadata Management

Managing large sets of videos can be a challenging task. Videos can come from a variety of sources and it can be easy to mislabel or confuse similar data. Loopy includes powerful features for attaching structured metadata to videos and then using this metadata for organization and navigation throughout the site.

For example, you can define a project which requires entering the genotype, treatment, age, or any other label or tag you define, to all uploaded videos.

Projects describe the types of metadata that must be attached to videos.
Many types of metadata are available: numbers text, dates, etc.

When uploading a video you can choose to associate it with a project. In this case, Loopy ensures that you have filled out all the required metadata before making the video available for analysis.

Compulsory metadata enforcement can help with license, regulatory, or experimental protocol compliance by ensuring all necessary information is collected. If you have a large number of users or an on-site instance then it can be used to ensure all members of your research group adhere to the same naming, reporting and data management standards.

Until the required metadata has been supplied, videos are held in a special 'quarantine'.
If you have multiple projects you can choose to browse the site 'as this project'. This means that only videos, and data related to those videos, from the selected project are shown.

Prices

Loopy is available in a number of different packages and is payable either monthly or annually with credit card. If you would prefer to pay via bank transfer or other means, please contact us. Loopy is updated automatically every 30 days. New features are available to all subscribers at no additional cost.

The dedicated package includes a private cloud instance of Loopy and associated storage for your exclusive use. There is no artificial user-limit on dedicated instances.

Annual or multi-month subscriptions can be 'paused' and your data will not be deleted. If you are not using Loopy then you do not have to pay for it.

Basic

1 Gb Storage

1 Concurrent Processing Job(s)

Video Upload and Management
Basic Video Editing
Behavioral Coding
Annotation Tool
Analysis and Plotting
Image Processing
2D & 3D Tracking
Deep Learning
Pose Estimation

Mid

5 Gb Storage

2 Concurrent Processing Job(s)

Video Upload and Management
Basic Video Editing
Behavioral Coding
Annotation Tool
Analysis and Plotting
Image Processing
2D & 3D Tracking
Deep Learning
Pose Estimation

All

10 Gb Storage

3 Concurrent Processing Job(s)

Video Upload and Management
Basic Video Editing
Behavioral Coding
Annotation Tool
Analysis and Plotting
Image Processing
2D & 3D Tracking
Deep Learning
Pose Estimation

If you have special data protection requirements, very large volumes of data, or your own lab/group storage infrastructure for video data, you might be also interested in our on-site version.

Publications

  • Wenig, K., Pacher, L. & Bugnyar, T. Testing the contagious nature of allopreening: bystander ravens are affected by conspecifics’ affiliative interactions.  Animal Behaviour  (2022). 10.1016/j.anbehav.2021.12.009
  • Demartsev, V., Manser, M. B., & Tattersall, G. J. Vocalization-associated respiration patterns: thermography-based monitoring and detection of preparation for calling.  Journal of Experimental Biology  (2022). 10.1242/jeb.243474
  • Völter, C., Starić, D. & Huber, L. Using machine learning to track dogs’ exploratory behavior in presence and absence of their caregiver.  arXiv  (2022). 10.31234/osf.io/5x8m2
  • Rajan, V., Babu, V., Häfker, N. S., Arboleda, E., Poehn, B., Gossenreiter, T., Gerrard, E., Hofbauer, M., Mühlestein, C., Bileck, A., Gerner, C., Ribera D'Alcala, M., Buia, M. C., Hartl, M., Lucas, R. J. & Tessmar-Raible, K. Seasonal variation in UVA light drives hormonal and behavioural changes in a marine annelid via a ciliary opsin.  Nature Ecology & Evolution  (2021). 10.1038/s41559-020-01356-1
  • Zekoll T, Waldherr M and Tessmar-Raible K Characterization of tmt-opsin2 in Medaka Fish Provides Insight Into the Interplay of Light and Temperature for Behavioral Regulation.  Frontiers in Physiology  (2021). 10.3389/fphys.2021.726941
  • Loftus, J., Harel, R., Nuñez, C., & Crofoot, M. Ecological and social pressures interfere with homeostatic sleep regulation in the wild.  bioRxiv  (2021). 10.1101/2021.09.26.461870
  • Robinson, L. M., Martínez, M., Leverett, K. L., Rossettie, M. S., Wilson, B. J., & Brosnan, S. F. Anything for a cheerio: Brown capuchins ( Sapajus [Cebus] apella ) consistently coordinate in an Assurance Game for unequal payoffs.  American Journal of Primatology  (2021). 10.1002/ajp.23321
  • Pedretti, G., Wirobski, G., Range, F., & Marshall-Pescini, S. Artificially elevated oxytocin concentrations in pet dogs are associated with higher proximity-maintenance and gazing towards the owners.  Physiology & Behavior  (2021). 10.1016/j.physbeh.2021.113451
  • Janisch, J., Mitoyen, C., Perinot, E., Spezie, G., Fusani, L. & Quigley, C. Video Recording and Analysis of Avian Movements and Behavior: Insights from Courtship Case Studies.  Integrative and Comparative Biology  (2021). 10.1093/icb/icab095
  • Matchette, S. R. & Herbert-Read, J. E. Dynamic visual noise promotes social attraction, but does not affect group size preference, in a shoaling fish.  Animal Behaviour  (2021). 10.1016/j.anbehav.2021.04.017
  • Ersoy, S., Maag, N., Boehly, T., Boucherie, P. H., & Bugnyar, T. Sex-specific parental care during postfledging in common ravens.  Animal Behaviour  (2021). 10.1016/j.anbehav.2021.09.004
  • Revilla-i-Domingo, R., Rajan, V. B. V., Waldherr, M., Prohaczka, G., Musset, H., Orel, L., Gerrard, E., Smolka, M., Stockinger, A., Farlik, M., Lucas, R. J., Raible, F., & Tessmar-Raible, K. Analyses of Cephalic and Non-cephalic Sensory Cell Types Provide Insight into Joint Photo- and Mechanoreceptor Evolution.  Life Science Weekly  (2021). 10.7554/elife.66144
  • Janisch, J., Perinot, E., Fusani, L. & Quigley, C. Deciphering choreographies of elaborate courtship displays of golden-collared manakins using markerless motion capture.  Ethology  (2021). 10.1111/eth.13161
  • Mitoyen, C., Quigley, C., Boehly, T. & Fusani L. Female behaviour is differentially associated with specific components of multimodal courtship in ring doves.  Animal Behaviour  (2021). 10.1016/j.anbehav.2020.12.014
  • Günzel, Y., McCollum, J., Paoli, M., Galizia, C. G., Petelski, I. & Couzin-Fuchs E. Social modulation of individual preferences in cockroaches.  iScience  (2020). 10.1016/j.isci.2020.101964
  • Kil, N., Ertelt, K. & Auer, U. Development and Validation of an Automated Video Tracking Model for Stabled Horses.  Animals  (2020). 10.3390/ani10122258
  • Rodin, B. Ispitivanje uloge L-tipa Kriptokoroma na cirkalunarno i svjetlom uvjetovano ponašanje u morskom kolutićavom crvu Platynereis dumerilii.  (University of Rijeka, 2018). https://urn.nsk.hr/urn:nbn:hr:193:700539
  • Ayers, T. Non-visual photoreception by G₀-type opsin in the annelid Platynereis dumerilii.  (University of Vienna. Zentrum für Molekulare Biologie, 2018). 10.25365/thesis.54364