Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings

Rathore, A., Sharma, A., Shah, S., Sharma, N., Torney, C. and Guttal, V. (2023) Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings. PeerJ, 11, e15573. (doi: 10.7717/peerj.15573) (PMID:37397020) (PMCID:PMC10309051)

Abstract

Aerial imagery and video recordings of animals are used for many areas of research such as animal behaviour, behavioural neuroscience and field biology. Many automated methods are being developed to extract data from such high-resolution videos. Most of the available tools are developed for videos taken under idealised laboratory conditions. Therefore, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. Methods that are useful for field conditions are often difficult to implement and thus remain inaccessible to empirical researchers. To address this gap, we present an open-source package called Multi-Object Tracking in Heterogeneous environments (MOTHe), a Python-based application that uses a basic convolutional neural network for object detection. MOTHe offers a graphical interface to automate the various steps related to animal tracking such as training data generation, animal detection in complex backgrounds and visually tracking animals in the videos. Users can also generate training data and train a new model which can be used for object detection tasks for a completely new dataset. MOTHe doesn’t require any sophisticated infrastructure and can be run on basic desktop computing units. We demonstrate MOTHe on six video clips in varying background conditions. These videos are from two species in their natural habitat—wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track individuals in all these videos. MOTHe is available as an open-source GitHub repository with a detailed user guide and demonstrations at: https://github.com/tee-lab/MOTHe-GUI.

• Item Type: Articles
• Additional Information: This work was supported by the DBT-IISc partnership program and infrastructure support from DST-FIST, by MHRD with a Ph.D. scholarship and by UGC-UKIERI with a collaborative research grant between Vishwesha Guttal and Colin Torney.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Torney, Professor Colin
• Authors: Rathore, A., Sharma, A., Shah, S., Sharma, N., Torney, C., and Guttal, V.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Mathematics
• Journal Name: PeerJ
• Publisher: PeerJ
• ISSN: 2167-8359
• ISSN (Online): 2167-8359
• Copyright Holders: Copyright © 2023 Rathore et al.
• First Published: First published in PeerJ 11: e15573
• Publisher Policy: Reproduced under a Creative Commons License
• Data DOI: 10.6084/m9.figshare.11980356.v3