AI Animal Identification via Visual Embeddings
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Solution Overview
Problem
Current animal management systems rely on manual and inefficient methods for tracking and assessing livestock, leading to challenges such as tag loss, inaccurate weight measurement, and stress caused by physical immobilization, which can result in suboptimal food planning, medication, and increased expenses.
Innovation Solution
An animal management system utilizing imaging devices and AI pipelines with models like SSD, FasterRCNN, and Triplet Loss Siamese Networks for animal identification, classification, and assessment, generating embeddings and confidence scores for efficient tracking and health monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RFID tags are used for animal tracking, then identification capability is improved, but tag loss rate increases due to short sensing range and ease of loss
Solution Approach 1:
The patent replaces physical RFID tags with computer vision-based identification. Imaging devices capture images of animals, and AI algorithms automatically identify and track them without requiring physical tags. This eliminates tag loss entirely while maintaining reliable identification capability through facial recognition and other visual features.
Solution Approach 2:
The system creates digital copies (images) of animals for identification purposes. Instead of relying on physical tags that can be lost, the system captures and stores visual images of animals, processing these images through AI pipelines to generate embeddings for identification. This digital copying approach eliminates the need for physical tags.
2Device complexity
If manual weighing methods are used, then equipment simplicity is improved, but measurement accuracy deteriorates leading to suboptimal food planning and medication
Solution Approach 1:
The patent replaces mechanical weighing equipment with computer vision-based weight estimation. Imaging devices capture images of animals, and AI algorithms analyze body dimensions, shape, and visual characteristics to estimate weight non-invasively. This eliminates the need for physical scales while providing accurate weight measurements for feed and medication planning.
3Measurement precision
If physical immobilization methods are used for assessment, then measurement accuracy is improved, but animal stress increases and time consumption increases
Solution Approach 1:
The patent replaces physical immobilization devices (head gates, restraining equipment) with computer vision-based assessment. Imaging devices capture images of animals in their natural state, and AI algorithms analyze body posture, gait, and visual features to assess health conditions, body condition scores, and other parameters. This eliminates animal stress and time-consuming restraint while maintaining assessment accuracy.
4Device complexity
If manual tracking methods are used, then system simplicity is improved, but productivity deteriorates making it impossible to track large numbers of livestock
Solution Approach 1:
The system enables automatic self-identification and self-tracking of animals. Imaging devices continuously capture images, and AI algorithms automatically process these images to identify, track, and record animal movements without human intervention. This automated self-service approach allows the system to efficiently track large herds of livestock that would be impossible to manage manually.
Solution Approach 2:
The patent employs powerful AI processing capabilities that accelerate the identification and tracking process. Advanced neural networks and embedding comparison algorithms rapidly analyze images and determine animal identities, enabling the system to process thousands of animals per hour with high accuracy, far exceeding manual tracking capabilities.
Data Source
AI summary
An animal management system has one or more imaging devices, and a computing device coupled to the one or more image devices for receiving one or more images captured by the one or more imaging devices, processing at least one image using an artificial intelligence (AI) pipeline for: (i) detecting and locating in the image one or more animals, (ii) for each detected animal: (a) generating at least one section of the detected animal, (b) determining a plurality of key points in each section, (c) generating an embedding for each section based on the plurality of key points in the section, and (d) combining the embeddings for generating an identification of the detected animal with a confidence score. Key points and bounding boxes may also have associated confidence scores.