22 docs tagged with "Animal-Ecology"

The Camera Traps application is both a simulator and IoT device software for utilizing machine learning on the edge in field research. The first implementation specializes in applying computer vision (detection and classification) to wildlife images for animal ecology studies. Two operational modes are supported (1) an input dataset of images to act as the images that would be generated an IoT camera device or (2) an input video file that would be captured by a camera which is then processed by an image detecting plugin that saves frames with motion in them; it uses these images to drive the simulation.

This repo provides instructions for extracting workload information from AI-Driven Animal Ecology (ADAE) studies.

The ctcontroller tool can be used to manage the provisioning and releasing of edge hardware as well as running and shutting down the camera-traps application.

Figure: Workflow of AI-Driven Animal Ecology Study.png)

Architectual Overview

Architecture Overview

TapisUI provides a research oriented frontend to interact with Tapis and tenant components. In this case, the ICICLE extension extends TapisUI with custom branding

Definitions of Key Terms and Concepts

System Architecture and Design Philosophy

Quick Start

Installation

Step 1: Extract arrival rates from real ADAE Studies

Quick Guide

View the main TapisUI wiki to learn how to deploy and test TapisUI extensions locally.

This section walks through how to use this repository and its features. It is split into sections based on the types of tasks you're looking to accomplish.

Continuing Multi-Session Missions

The Decentralized Microservice Drone System for Digital Agriculture is a distributed, scalable platform designed to orchestrate autonomous drone operations for agricultural field missions. The system captures, processes, and analyzes aerial imagery and video data to support precision agriculture, crop monitoring, and field management operations.

This tapis ui extension enables additional icicle specific branding and tabs on tapisui.

Prerequisites

Getting Started with WildWing

This work presents a modular software pipeline and end-to-end workflow for video-based animal re-identification, which assigns consistent individual IDs by clustering multiview spatio-temporal tracks with minimal human intervention. Starting from raw video, the system detects and tracks animals, scores and selects informative left/right views, computes embeddings, clusters annotations by viewpoint, and then links clusters across time and varying perspectives using spatio-temporal continuity. Automated consistency checks resolve remaining ambiguities. Preliminary experiments demonstrate near-perfect identification accuracy with very limited manual verification. The workflow is designed to be generalizable across species. Currently, trained models support Grevy’s and Plains zebras, with plans to expand to a broader range of species.

An open-source, autonomous and affordable UAS for animal behaviour video monitoring using Parrot Anafi drones to track group-living animals.