Animal Tracking via Ellipsoid Shape Consistency
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Solution Overview
Problem
Current precision livestock farming systems face challenges in accurately tracking individual animals in group-housed environments, as existing methods are limited by battery life, size, cost, durability, and wireless constraints of wearable devices, and computer vision systems struggle to differentiate between individual animals within a collective group.
Innovation Solution
A multi-object tracking system that uses a motion sensing device to receive image frames, generates ellipsoid models for each animal based on defined surface points, and tracks their position and orientation by enforcing shape consistency, providing data for livestock management systems to monitor health, well-being, and aggression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearable devices are used to track individual animals, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces wearable mechanical/electronic tracking devices with a computer vision-based optical system. The motion sensing device captures images and the system processes visual data to track animal positions and behaviors, eliminating the need for physical tags, sensors, and batteries on each animal while achieving comparable or superior tracking precision.
Solution Approach 2:
The computer vision system serves multiple functions simultaneously: it tracks individual animal positions, monitors behaviors, estimates weights, and detects health issues. A single motion sensing device and processing system replaces multiple specialized wearable devices, reducing overall system complexity while maintaining measurement precision.
2Measurement precision
If wearable devices are affixed to animals, then individual animal monitoring is improved, but ease of operation deteriorates due to battery life and durability constraints
Solution Approach 1:
The system replaces battery-powered wearable devices with a centralized computer vision system. The motion sensing device continuously captures images without interruption, and the processing system maintains tracking across frames, eliminating concerns about battery life, charging cycles, and device durability that plague wearable approaches.
Solution Approach 2:
The computer vision system provides continuous, uninterrupted monitoring by capturing sequential images and maintaining track associations across frames. The system can operate indefinitely without interruption, unlike wearable devices that require periodic battery replacement or recharging, ensuring operational continuity.
3Productivity
If computer vision systems treat animals as a collective group, then productivity is improved through automated monitoring, but measurement precision deteriorates because individual animal tracking is lost
Solution Approach 1:
The system segments the collective group of animals into individually tracked objects by detecting distinct features in each animal's appearance, posture, and movement patterns. The computer vision algorithm maintains separate track files for each animal, enabling individual identification and monitoring while processing the entire group automatically, thus achieving both high productivity and precise individual measurement.
4Duration of action of stationary object
If multi-object tracking is implemented for long-term continuous monitoring, then duration of action is improved, but device complexity increases
Solution Approach 1:
The patent implements long-term continuous tracking through a computer vision system that processes sequential images and maintains track associations over time. The system uses algorithms to handle animal entry/exit from the field of view and maintains consistent identification across extended periods without the mechanical complexity or battery limitations of wearable devices.
Data Source
AI summary
Implementations directed to providing a computer-implemented method comprising receiving, from a motion sensing device, a plurality of image frames that includes information regarding a plurality of animals housed in a group-housed environment, determining a coordinate space of the group-housed environment based on an analysis of a first image frame of the image frames, generating, based on the analysis of the first image frame, an ellipsoid model for each animal based on defined surface points for each animal weighted according to a likely proximity to a crest of a spine of the respective animal, and tracking a position and an orientation of each animal within the image frames by enforcing shape consistency of the ellipsoid models, and adjusting the position of each of the ellipsoid models based on the defined surface points for each animal and a maximum likelihood formulation of a movement distance for each animal.