Computer Vision for Zoo Management: Learning from Animals’ Life Patterns to Enhance Welfare
Hardware: NVIDIA Jetson Orin Nano
Application: Animal Behavior Analysis & Habit Pattern Tracking
Industry: Zoo Management
Deployment Location: US, Australia
As a strong basement data resource supporting wildlife conservation, education, and research, the zoo plays a crucial role dedicated to the preservation of the earth’s diverse ecosystems. To comprehensively safeguard the animals, an in-depth understanding of their daily routines, behaviors, and habitat utilization is imperative.
Traditionally, the task of monitoring animals within zoo enclosures has rested heavily on manual observations – a process often plagued by its own set of challenges, including time constraints, labor intensiveness, and the inherent potential for human error. We need more precise and efficient methods to keep tracking their status, not to mention the protection of animal health is also one of the important topics of the SDGs.
Challenge
First and foremost, the collection and processing of vast amounts of data generated by multiple animal enclosures must be streamlined to ensure efficiency. Additionally, the development of robust algorithms capable of distinguishing normal from abnormal behavior is pivotal. Equally crucial is the integration of real-time alerts and notifications to enable immediate response to any deviations, guaranteeing the animals’ well-being. It’s essential not only to track individual animal behavior but also to identify broader trends and patterns for making informed, data-driven decisions.
Solution
The proposed solution leverages cutting-edge computer vision AI models, deployed on the powerful NVIDIA Jetson Orin Nano, to optimize zoo management. Employing object detection, image classification, and feature extraction techniques, it adeptly identifies and tracks animals within their enclosures, thus eliminating the potential for observer bias and enabling the capture of precise and detailed behavioral data.
Furthermore, the integration of RNNs, particularly LSTM variants, empowers the system to analyze sequential data, unlocking the ability to recognize abnormal behavior by detecting deviations from learned patterns. This continuous monitoring provides a comprehensive understanding of animal behavior day and night, facilitating prompt interventions when anomalies arise. Beyond ensuring animal welfare, this real-time data serves as a valuable resource for tailoring enrichment activities to the preferences and behaviors of individual animals, ultimately contributing to optimized habitat design that nurtures the well-being and natural behaviors of the zoo’s residents.
Take an exercise through our wiki guidance on the alwaysAI platform to explore the object detection model for animal recognition first!
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