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

PeerJ

article

Akanksha Rathore¹  Ananth Sharma¹  Shaan Shah²  Nitika Sharma¹  Colin Torney⁴  Vishwesha Guttal¹ 

[1] Centre for Ecological Sciences, Indian Institute of Science;Department of Electrical Engineering, Indian Institute of Technology;Department of Ecology and Evolutionary Biology, University of California;School of Mathematics and Statistics, University of Glasgow

关键词: Animal behaviour;    Automated tracking;    Computer vision;    Convolutional neural network;    Machine learning;    Multi-animal tracking;    Tracking in natural habitat;   

DOI  :  10.7717/peerj.15573

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY