AI Animal Identification via Dynamic Capture and Hierarchical ML
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Solution Overview
Problem
Conventional animal recognition technologies are static and inaccurate, particularly for fine-grained identification, and fail in realistic settings due to the difficulty in capturing high-quality images of animals, which are often uncooperative.
Innovation Solution
A check-in kiosk equipped with a camera device having an arm to hold objects, such as treats, to orient animals for optimal image capture, combined with machine learning models for accurate animal identification and AI-driven recommendation generation based on animal records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing is used for animal identification, then the system is simple to implement, but the identification accuracy is low and cannot achieve fine-grained identification
Solution Approach 1:
The patent divides the animal identification task into multiple hierarchical levels: species classification, breed identification, and individual animal recognition. This segmentation allows the system to progressively refine identification accuracy from coarse to fine-grained, resolving the contradiction between simple implementation and high accuracy by breaking down the complex task into manageable stages.
Solution Approach 2:
The patent employs dynamic image capture techniques including multiple angles, distances, and sequences of images rather than relying on a single static image. This dynamic approach enables the system to capture sufficient information for accurate identification even when animals are uncooperative, thereby improving measurement precision without requiring overly complex controlled imaging setups.
2Measurement precision
If high-quality images are captured for accurate identification, then the identification precision improves, but the ease of operation deteriorates because animals are uncooperative and difficult to position
Solution Approach 1:
The patent uses automated image capture systems and machine learning algorithms that automatically select, process, and evaluate images without requiring manual intervention to position or pose the animal. The system self-adjusts to capture sufficient images from various angles and automatically identifies the best quality images for identification, thereby maintaining high measurement precision while improving ease of operation.
Solution Approach 2:
The patent changes multiple parameters including image capture angle, distance, sequence, and lighting conditions to optimize image quality automatically. By dynamically adjusting these parameters through automated systems rather than manual intervention, the system achieves high-quality images for accurate identification while maintaining ease of operation even with uncooperative animals.
3Reliability
If static image recognition is used, then the system is simple, but the reliability is low in realistic settings where animals move and cannot be positioned properly
Solution Approach 1:
The patent transitions from static single-image recognition to dynamic multi-image sequence analysis. The system captures images from multiple angles, distances, and time points, then uses machine learning to synthesize this dynamic data into reliable identification results. This dynamic approach significantly improves reliability in realistic settings where animals move freely, while the modular architecture keeps system complexity manageable.
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning model continuously evaluates captured images, identifies quality and positioning issues, and guides subsequent image capture to improve identification reliability. This closed-loop feedback system automatically adjusts the imaging process based on real-time conditions, enhancing reliability without requiring complex manual intervention or overly sophisticated hardware.
4Loss of information
If fine-grained animal identification is achieved, then the loss of information is reduced, but the device complexity increases due to the need for multiple machine learning models
Solution Approach 1:
The patent segments the identification task into distinct hierarchical levels (species, breed, individual) with specialized machine learning models for each level. This segmentation reduces information loss at each stage by focusing computational resources on specific identification aspects, while the modular structure manages complexity by organizing multiple models into a coherent hierarchical framework rather than requiring a single monolithic complex system.
Solution Approach 2:
The patent designs machine learning models with multi-functionality where lower-level models (species classification) serve as foundations for higher-level models (breed and individual identification). This universal approach allows the system to efficiently progress from coarse to fine-grained identification, minimizing information loss while managing complexity through reusable and hierarchical model architecture rather than entirely separate systems.
Data Source
AI summary
Techniques for animal identification based on animal behavior. A first video depicting a first animal associated with a user during a first visit to an enterprise location is received. One or more attributes pertaining to the first animal or to the user are received. A first behavior of the first animal as depicted in the first video is extracted and encoded into a first behavioral representation. One or more machine learning models are trained to identify the first animal based on the first behavioral representation and the one or more attributes.