Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A review of 28 free animal-tracking software applications: current features and limitations

Abstract

Well-quantified laboratory studies can provide a fundamental understanding of animal behavior in ecology, ethology and ecotoxicology research. These types of studies require observation and tracking of each animal in well-controlled and defined arenas, often for long timescales. Thus, these experiments produce long time series and a vast amount of data that require the use of software applications to automate the analysis and reduce manual annotation. In this review, we examine 28 free software applications for animal tracking to guide researchers in selecting the software that might best suit a particular experiment. We also review the algorithms in the tracking pipeline of the applications, explain how specific techniques can fit different experiments, and finally, expose each approach’s weaknesses and strengths. Our in-depth review includes last update, type of platform, user-friendliness, off- or online video acquisition, calibration method, background subtraction and segmentation method, species, multiple arenas, multiple animals, identity preservation, manual identity correction, data analysis and extra features. We found, for example, that out of 28 programs, only 3 include a calibration algorithm to reduce image distortion and perspective problems that affect accuracy and can result in substantial errors when analyzing trajectories and extracting mobility or explored distance. In addition, only 4 programs can directly export in-depth tracking and analysis metrics, only 5 are suited for tracking multiple unmarked animals for more than a few seconds and only 11 have been updated in the period 2019–2021.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Overview of typical setups used in tracking experiments.
Fig. 2: Illustration showing the general workflow of a tracking program: how an image frame is processed from video to analysis.
Fig. 3: Tracking software comparison: last update, platform, video acquisition, calibration and detection.
Fig. 4: Tracking software comparison: trajectory generation, data analysis and extra features.

References

  1. 1.

    Dell, A. I. et al. Automated image-based tracking and its application in ecology. Trends Ecol. Evol. 29, 417–428 (2014).

    PubMed  Article  Google Scholar 

  2. 2.

    Hajar, R. Animal testing and medicine. Heart. Views 12, 42 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Carola, V., D’Olimpio, F., Brunamonti, E., Mangia, F. & Renzi, P. Evaluation of the elevated plus-maze and open-field tests for the assessment of anxiety-related behaviour in inbred mice. Behav. Brain Res. 134, 49–57 (2002).

    PubMed  Article  Google Scholar 

  4. 4.

    Olton, D. S. Mazes, maps, and memory. Am. Psychol. 34, 583–596 (1979).

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Silverman, J. L., Babineau, B. A., Oliver, C. F., Karras, M. N. & Crawley, J. N. Influence of stimulant-induced hyperactivity on social approach in the BTBR mouse model of autism. Neuropharmacology 68, 210–222 (2013).

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Cirulli, F., Berry, A. & Alleva, E. Intracerebroventricular administration of brain-derived neurotrophic factor in adult rats affects analgesia and spontaneous behaviour but not memory retention in a Morris Water Maze task. Neurosci. Lett. 287, 207–210 (2000).

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Borta, A. & Schwarting, R. K. W. Inhibitory avoidance, pain reactivity, and plus-maze behavior in Wistar rats with high versus low rearing activity. Physiol. Behav. 84, 387–396 (2005).

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Kulesskaya, N. & Voikar, V. Assessment of mouse anxiety-like behavior in the light–dark box and open-field arena: role of equipment and procedure. Physiol. Behav. 133, 30–38 (2014).

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Lee, H., Iida, T., Mizuno, A., Suzuki, M. & Caterina, M. J. Altered thermal selection behavior in mice lacking transient receptor potential vanilloid 4. J. Neurosci. 25, 1304–1310 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Woodley, C. M., Urbanczyk, A. C., Smith, D. L. & Lemasson, B. H. Integrating visual psychophysical assays within a Y-Maze to isolate the role that visual features play in navigational decisions. J. Vis. Exp. 147, e59281 (2019).

    Google Scholar 

  11. 11.

    Jonsson, M. et al. High-speed imaging reveals how antihistamine exposure affects escape behaviours in aquatic insect prey. Sci. Total Environ. 648, 1257–1262 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  12. 12.

    Dankert, H., Wang, L., Hoopfer, E. D., Anderson, D. J. & Perona, P. Automated monitoring and analysis of social behavior in Drosophila. Nat. Methods 6, 297–303 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Pereira, T. D. et al. Fast animal pose estimation using deep neural networks. Nat. Methods 16, 117–125 (2019).

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Graving, J. M. et al. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 8, e47994 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Franco-Restrepo, J. E., Forero, D. A. & Vargas, R. A. A review of freely available, open-source software for the automated analysis of the behavior of adult zebrafish. Zebrafish 16, 223–232 (2019).

    PubMed  Article  Google Scholar 

  17. 17.

    Husson, S. J., Costa, W. S., Schmitt, C. & Gottschalk, A. Keeping track of worm trackers. in WormBook: The Online Review of C. elegans Biology (WormBook, 2018).

  18. 18.

    Robie, A. A., Seagraves, K. M., Egnor, S. E. R. & Branson, K. Machine vision methods for analyzing social interactions. J. Exp. Biol. 220, 25–34 (2017).

    PubMed  Article  Google Scholar 

  19. 19.

    Sridhar, V. H., Roche, D. G. & Gingins, S. Tracktor: image-based automated tracking of animal movement and behaviour. Methods Ecol. Evol. 10, 815–820 (2018).

    Article  Google Scholar 

  20. 20.

    Henry, J., Rodriguez, A. & Wlodkowic, D. Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology. PeerJ 7, e7367 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Pérez-Escudero, A., Vicente-Page, J., Hinz, R. C., Arganda, S. & de Polavieja, G. G. idTracker: tracking individuals in a group by automatic identification of unmarked animals. Nat. Methods 11, 743–748 (2014).

    PubMed  Article  CAS  Google Scholar 

  22. 22.

    Rodriguez, A. et al. ToxTrac: a fast and robust software for tracking organisms. Methods Ecol. Evol. 9, 460–464 (2018).

    Article  Google Scholar 

  23. 23.

    Junior, C. F. C. et al. ETHOWATCHER: validation of a tool for behavioral and video-tracking analysis in laboratory animals. Comput. Biol. Med. 42, 257–264 (2012).

    Article  Google Scholar 

  24. 24.

    Crispim Junior, C. F. et al. EthoWatcher. http://ethowatcher.paginas.ufsc.br/ (2019).

  25. 25.

    Samson, A. L. et al. MouseMove: an open source program for semi-automated analysis of movement and cognitive testing in rodents. Sci. Rep. 5, 16171 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Gal, A., Saragosti, J. & Kronauer, D. J. C. anTraX, a software package for high-throughput video tracking of color-tagged insects. eLife 9, e58145 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Wang, S. H., Cheng, X. E., Qian, Z.-M., Liu, Y. & Chen, Y. Q. Automated planar tracking the waving bodies of multiple zebrafish swimming in shallow water. PLoS ONE 11, e0154714 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. 28.

    Rodriguez, A., Zhang, H., Klaminder, J., Brodin, T. & Andersson, M. ToxId: an efficient algorithm to solve occlusions when tracking multiple animals. Sci. Rep. 7, 14774 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  29. 29.

    Rodriguez, A. et al. ToxTrac. https://sourceforge.net/projects/toxtrac/ (2019).

  30. 30.

    Pérez-Escudero, A., Vicente-Page, J., Hinz, R. C., Arganda, S. & de Polavieja, G. G. idTracker. http://www.idtracker.es/ (2019).

  31. 31.

    Romero-Ferrero, F., Bergomi, M. G., Hinz, R., Heras, F. J. H. & de Polavieja, G. G. idtracker.ai: tracking all individuals in large collectives of unmarked animals. Nat. Methods 16, 179–182 (2019).

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Romero-Ferrero, F., Bergomi, M. G., Hinz, R., Heras, F. J. H. & de Polavieja, G. G. idtracker.ai. https://idtracker.ai/ (2019).

  33. 33.

    Branson, K., Robie, A. A., Bender, J., Perona, P. & Dickinson, M. H. High-throughput ethomics in large groups of Drosophila. Nat. Methods 6, 451–457 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Branson, K., Robie, A. A., Bender, J., Perona, P. & Dickinson, M. H. Ctrax: the Caltech multiple walking fly tracker. http://ctrax.sourceforge.net/ (2019).

  35. 35.

    Shin, M. C. ABCTracker. http://abctracker.org/ (2018).

  36. 36.

    Rice, L. A. A beginning-to-end system for efficiently gathering tracking data on multiple targets. Thesis, University of North Carolina at Charlotte (2016).

  37. 37.

    Farynyk, D. ABC Tracker Support. https://abctracker.atlassian.net/wiki/spaces/ABCTS/pages/458795/FAQ (2020).

  38. 38.

    Patman, J., Michael, S. C. J., Lutnesky, M. M. F. & Palaniappan, K. BioSense: real-time object tracking for animal movement and behavior research. IEEE Applied Imagery Pattern Recognition Workshop (AIPR) 1–8 (2018).

  39. 39.

    Patman, J., Michael, S. C. J., Lutnesky, M. M. F. & Palaniappan, K. BioSense. https://ieeexplore.ieee.org/document/8707411 (2018).

  40. 40.

    Werkhoven, Z., Rohrsen, C., Qin, C., Brembs, B. & de Bivort, B. MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology. PLoS ONE 14, e0224243 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Werkhoven, Z., Rohrsen, C., Qin, C., Brembs, B. & de Bivort, B. MARGO. https://github.com/de-Bivort-Lab/margo (2020).

  42. 42.

    Swierczek, N. A., Giles, A. C., Rankin, C. H. & Kerr, R. A. High-throughput behavioral analysis in C. elegans. Nat. Methods 8, 592–598 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Swierczek, N. A., Giles, A. C., Rankin, C. H. & Kerr, R. A. Multi-Worm Tracker. https://sourceforge.net/projects/mwt/ (2019).

  44. 44.

    Correll, N. et al. SwisTrack: a tracking tool for multi-unit robotic and biological systems. IEEE/RSJ International Conference on Intelligent Robots and Systems 2185–2191 (2006).

  45. 45.

    Mario, E. Di et al. SwisTrack. https://sourceforge.net/projects/swistrack (2019).

  46. 46.

    Zhang, Z. A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000).

    Article  Google Scholar 

  47. 47.

    Rao, S. R. et al. Small animal video tracking for activity and path analysis using a novel open-source multi-platform application (AnimApp). Sci. Rep. 9, 12343 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. 48.

    Pennekamp, F., Schtickzelle, N. & Petchey, O. L. BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes. Ecol. Evol. 5, 2584–2595 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Pennekamp, F., Schtickzelle, N. & Petchey, O. L. BEMOVI. http://bemovi.info/ (2015).

  50. 50.

    Harmer, A. M. T. & Thomas, D. B. Pathtrackr: an r package for video tracking and analysing animal movement. Methods Ecol. Evol. 10, 1196–1202 (2019).

    Article  Google Scholar 

  51. 51.

    Harmer, A. M. T. & Thomas, D. B. Pathtrackr. https://github.com/aharmer/pathtrackr (2019).

  52. 52.

    Madan, C. R. & Spetch, M. L. Visualizing and quantifying movement from pre-recorded videos: the spectral time-lapse (STL) algorithm. F1000Res. 3, 19 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Mönck, H. J. et al. BioTracker: an open-source computer vision framework for visual animal tracking. Preprint at https://arxiv.org/abs/1803.07985 (2019).

  54. 54.

    Yamanaka, O. & Takeuchi, R. UMATracker: an intuitive image-based tracking platform. J. Exp. Biol. 221, jeb182469 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  55. 55.

    Yamanaka, O. & Takeuchi, R. UMATracker. http://ymnk13.github.io/UMATracker/ (2019).

  56. 56.

    Geuther, B. Q. et al. Robust mouse tracking in complex environments using neural networks. Commun. Biol. 2, 124 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Geuther, B. Q. et al. MouseTracking. https://github.com/KumarLabJax/MouseTracking (2019).

  58. 58.

    Zhiping, X. U. & Cheng, X. E. Zebrafish tracking using convolutional neural networks. Sci. Rep. 7, 42815 (2017).

    Article  CAS  Google Scholar 

  59. 59.

    Itskovits, E., Levine, A., Cohen, E. & Zaslaver, A. A multi-animal tracker for studying complex behaviors. BMC Biol. 15, 29 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  60. 60.

    Itskovits, E., Levine, A., Cohen, E. & Zaslaver, A. Multi-Animal Tracker. https://github.com/itskov/MultiAnimalTrackerSuite (2019).

  61. 61.

    Cuevas, E. V., Zaldivar, D. & Rojas, R. Kalman filter for vision tracking. Freie Universität Berlin, Fachbereich Mathematik und Informatik; Serie B, Informatik (2005).

  62. 62.

    Kuhn, H. W. The Hungarian method for the assignment problem. Nav. Res. Logist. Q. 2, 83–97 (1955).

    Article  Google Scholar 

  63. 63.

    Del Moral, P. Nonlinear filtering: interacting particle resolution. C R Acad. Sci. I 325, 653–658 (1997).

    Article  Google Scholar 

  64. 64.

    Ohayon, S., Avni, O., Taylor, A. L., Perona, P. & Egnor, S. E. R. Automated multi-day tracking of marked mice for the analysis of social behaviour. J. Neurosci. Methods 219, 10–19 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Ohayon, S., Avni, O., Taylor, A. L., Perona, P. & Egnor, S. E. R. motr: a MOuse TRacker. http://motr.janelia.org/ (2019).

  66. 66.

    Rodriguez, A. et al. Optical fish trajectory measurement in fishways through computer vision and artificial neural networks. J. Comput. Civ. Eng. 25, 291–301 (2011).

    Article  Google Scholar 

  67. 67.

    Rodriguez, A., Bermúdez, M., Rabuñal, J. & Puertas, J. Fish tracking in vertical slot fishways using computer vision techniques. J. Hydroinformatics 17, 275–292 (2014).

    Article  Google Scholar 

  68. 68.

    Sridhar, V. H., Roche, D. G. & Gingins, S. Tracktor. https://github.com/vivekhsridhar/tracktor (2019).

  69. 69.

    Rao, G. M. & Satyanarayana, C. Visual object target tracking using particle filter: a survey. Int. J. Image Graph Signal Process. 6, 57–71 (2013).

    Article  Google Scholar 

  70. 70.

    Datta, S. R. Q&A: understanding the composition of behavior. BMC Biol. 17, 44 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Kabra, M., Robie, A. A., Rivera-Alba, M., Branson, S. & Branson, K. JAABA: interactive machine learning for automatic annotation of animal behavior. Nat. Methods 10, 64–67 (2013).

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Linck, V. M. et al. Effects of inhaled Linalool in anxiety, social interaction and aggressive behavior in mice. Phytomedicine 17, 679–683 (2010).

    CAS  PubMed  Article  Google Scholar 

  73. 73.

    Steele, W. B., Mole, R. A. & Brooks, B. W. Experimental protocol for examining behavioral response profiles in larval fish: application to the neuro-stimulant caffeine. J. Vis. Exp. 137, e57938 (2018).

    Google Scholar 

  74. 74.

    Dankert, H. QTrack (Cadabra). http://www.vision.caltech.edu/cadabra/ (2009).

  75. 75.

    Krynitsky, J. et al. Rodent Arena Tracker (RAT): a machine vision rodent tracking camera and closed loop control system. eNeuro 7, ENEURO.0485-19.2020 (2020).

  76. 76.

    Krynitsky, J. et al. Rodent arena tracker (RAT). https://hackaday.io/project/162481-rodent-arena-tracker-rat (2020).

  77. 77.

    Feldman, A., Hybinette, M. & Balch, T. The multi-iterative closest point tracker: an online algorithm for tracking multiple interacting targets. J. Field Robot. 29, 258–276 (2012).

    Article  Google Scholar 

  78. 78.

    Hrolenok, B., Quitmeyer, A., Motter, S., Stolarsky, D. & Migliozzi, B. L. R. Bio-Tracking. http://www.bio-tracking.org/ (2012).

  79. 79.

    Risse, B., Berh, D., Otto, N., Klämbt, C. & Jiang, X. FIMTrack: an open source tracking and locomotion analysis software for small animals. PLoS Comput. Biol. 13, e1005530 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  80. 80.

    Risse, B., Berh, D., Otto, N., Klämbt, C. & Jiang, X. FIMTrack. https://www.uni-muenster.de/Informatik.AGRisse/media/fim-media.html (2017).

Download references

Author information

Affiliations

Authors

Contributions

A.R. co-wrote the manuscript and performed most of the analysis of the tracking software. V.P. co-wrote the manuscript and assisted with the analysis of the tracking software. J.H., D.W. and M.A. revised and edited the manuscript and assisted with the analysis of the tracking software.

Corresponding author

Correspondence to Alvaro Rodriguez.

Ethics declarations

Competing interests

The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Peer review information Lab Animal thanks Waseem Abbas, Alfonso Perez-Escudero and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Panadeiro, V., Rodriguez, A., Henry, J. et al. A review of 28 free animal-tracking software applications: current features and limitations. Lab Anim 50, 246–254 (2021). https://doi.org/10.1038/s41684-021-00811-1

Download citation

Search

Quick links