Google Gemini Agentic AI Vision for Wildlife Analytics

Wildlife Behavioral Risk Vision Agent - Google Gemini 3 Hackathon

Agentic Vision System using Google Gemini 3

An ethical, stateful, dual-agent wildlife monitoring system that detects persistent behavioral deviations relative to group norms, accumulates confidence over time, and visualizes at-risk animals using AI-seeded OpenCV tracking.

Built for conservation monitoring demos and the Google Gemini 3 Hackathon.

Demo

Problem

Wildlife conservation teams often rely on manual observation to detect:

• Injured animals
• Social exclusion
• Persistent behavioral anomalies
• Abnormal movement patterns

This process is:

• Time consuming
• Subjective
• Not scalable

We built a stateful agentic vision system that:

• Observes animals over time
• Learns group behavioral baselines
• Detects persistent deviations
• Escalates risk without making medical claims

System Overview

This project contains two cooperating AI agents:

1️⃣ Behavioral Reasoning Agent (Gemini 3 Pro)

Analyzes raw wildlife video and extracts structured behavioral signals:

• Stable individual_id
• Relative speed (0–1)
• Posture asymmetry (0–1)
• Distance from group center
• Group baseline averages
• Confidence accumulation
• At-risk seed (time window + normalized coordinates)

It outputs structured JSON memory for downstream reasoning.

2️⃣ Visualization Agent (Gemini 3 Pro)

Generates a complete standalone Python script that:

• Uses memory data as a tracking seed
• Seeks directly to the seed timestamp
• Converts normalized coordinates → pixels
• Initializes an OpenCV CSRT tracker
• Draws red bounding boxes for at-risk animals
• Saves annotated video output
• Captures sample frames

Workflow

Wildlife Video
      │
      ▼
Gemini 3 Vision Agent
(Beh