Turkey Behavior Identification System with a GUI Using Deep Learning and Video Analytics

TL;DR

This paper presents a deep learning-based video analytics system with a GUI for identifying turkey behaviors to assess welfare, automating what was traditionally manual and labor-intensive.

Contribution

The system integrates recent object detection and tracking advances with traditional image processing, providing an automated, user-friendly tool for turkey behavior analysis.

Findings

Automates turkey behavior identification from video data.

Uses deep learning and image processing for accurate detection.

Includes a web-based interface for visualization and analysis.

Abstract

In this paper, we propose a video analytics system to identify the behavior of turkeys. Turkey behavior provides evidence to assess turkey welfare, which can be negatively impacted by uncomfortable ambient temperature and various diseases. In particular, healthy and sick turkeys behave differently in terms of the duration and frequency of activities such as eating, drinking, preening, and aggressive interactions. Our system incorporates recent advances in object detection and tracking to automate the process of identifying and analyzing turkey behavior captured by commercial grade cameras. We combine deep-learning and traditional image processing methods to address challenges in this practical agricultural problem. Our system also includes a web-based user interface to create visualization of automated analysis results. Together, we provide an improved tool for turkey researchers to assess turkey welfare without the time-consuming and labor-intensive manual inspection.