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Multiple Animals Tracking in VideoUsing Part Affinity Fields

Multiple Animals Tracking in Video Using Part Affinity Fields Ivan F. Rodriguez∗ , Rémi Mégret† , Roian Egnor ‡ , Kristin Branson‡ Jose L. Agosto§ , Tugrul Giray§ and Edgar Acuña¶ ∗ Department of Mathematics, University of Puerto Rico, Rı́o Piedras campus † Department of Computer Science, University of Puerto Rico, Rı́o Piedras campus ‡ HHMI Janelia Research Campus § Department of Biology, University of Puerto Rico, Rı́o Piedras campus ¶ Department of Mathematical Sciences, University of Puerto Rico, Mayagüez campus Abstract—In this work, we address the problem of pose detection and tracking of multiple individuals for the study of behaviour in insects and animals. Using a Deep Neural Network architecture, precise detection and association of the body parts can be performed.

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Publication details

Organizations
🇵🇷 University of Puerto Rico
Year
2018
Type
Workshop

Relevancy to Gratheon

This paper is relevant to Gratheon because it informs entrance and behavior analytics in the Gratheon web app, camera-based hive-scanner and computer-vision models. Its methods and findings can be translated into product requirements for reliable field deployments: what should be sensed, how signals should be interpreted, and which uncertainty or validation limits need to be surfaced to beekeepers. For Gratheon, the work is most useful as an evidence-backed design reference for connecting local hive observations with actionable recommendations in the web app while keeping hardware practical for remote apiaries.