Jan 13, 2020

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

bioRxiv
Akanksha RathoreVishwesha Guttal

Abstract

Video recordings of animals are used for many areas of research such as collective movement, animal space-use, animal censuses and behavioural neuroscience. They provide us with behavioural data at scales and resolutions not possible with manual observations. Many automated methods are being developed to extract data from these high-resolution videos. However, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. We present an open-source end-to-end pipeline called Multi-Object Tracking in Heterogenous environments (MOTHe), a python-based application that uses a basic convolutional neural network for object detection. MOTHe allows researchers with minimal coding experience to track multiple animals in their natural habitats. It identifies animals even when individuals are stationary or partially camouflaged. MOTHe has a command-line-based interface with one command for each action, for example, finding animals in an image and tracking each individual. Parameters used by the algorithm are well described in a configuration file along with example values for different types of tracking scenario. MOTHe doesn't require any sophisticated infrastructure an...Continue Reading

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Mentioned in this Paper

Detection
Environmental Infrastructure
Source
Size
Antilope cervicapra
Experience
Wasps
Detected (Finding)
Video Media
Hemodialysis

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