Application of Machine Learning for Automating Behavioral Tracking of Captive Bornean Orangutans (Pongo Pygmaeus)

This study investigates the application of machine learning in the form of image classification and object detection to video material to automate behavior recognition in captive orangutans (Pongo pygmaeus). A machine learning model was constructed using a 2 min video consisting of 30 s clips of each selected behavior. The machine learning model had a 13% detection rate and showed potential for future expansion, with the goal of automating behavioral studies, but also notable issues that should be considered when using such methods. This article applies object detection to CCTV video material to investigate the potential of using machine learning to automate behavior tracking. This study includes video tapings of two captive Bornean orangutans and their behavior. From a 2 min training video containing the selected behaviors, 334 images were extracted and labeled using Rectlabel. The labeled training material was used to construct an object detection model using Create ML. The use of object detection was shown to have potential for automating tracking, especially of locomotion, whilst filtering out false positives. Potential improvements regarding this tool are addressed, and future implementation should take these into consideration. These improvements include using adequately diverse training material and limiting iterations to avoid overfitting the model.