Mouse Outline Tracking for Individual Identification in Group Activity
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Solution Overview
Problem
Conventional technologies struggle to identify individual animals, especially mice, in situations where they are constantly moving or in groups, and require additional equipment like cameras and thermographs, increasing costs and limiting applications beyond health management.
Innovation Solution
An information processing device that uses image acquisition, outline detection, individual identification, and model selection to analyze moving animals, employing skeleton estimation and individual identification models to identify and track mice within a predetermined activity range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional biometric processing technology is used to measure animal data contactlessly, then measurement capability is achieved, but individual identification capability in moving animals or groups is lost
Solution Approach 1:
The patent segments the animal's body into multiple key points (head, tail, limbs, etc.) and tracks each key point's movement trajectory separately. This segmentation allows the system to distinguish individual animals by their unique movement patterns and relative positions of body parts, enabling reliable identification even when animals are moving or in groups.
Solution Approach 2:
The patent transitions from static biometric measurement to dynamic spatiotemporal analysis by adding the time dimension and spatial relationship dimensions. By analyzing the movement trajectories and relative positions of body key points over time, the system can identify individuals based on their unique movement characteristics rather than relying solely on static biometric data.
2Reliability
If camera and thermograph equipment are added to check animal health conditions, then health monitoring capability is improved, but device cost and complexity increase
Solution Approach 1:
The patent makes the image processing device multi-functional by enabling it to perform both individual identification and health condition monitoring using the same hardware components. The skeleton estimation and movement analysis functions serve dual purposes: identifying animals individually while also detecting health-related movement abnormalities, eliminating the need for separate specialized equipment.
Solution Approach 2:
The system uses the movement data already captured for identification purposes to also assess health conditions. By analyzing the same skeleton estimation results and movement trajectories, the system can detect health issues without requiring additional sensors or equipment, making the existing system serve multiple functions.
3Loss of information
If conventional technology is used for animal identification, then static biometric data can be processed, but dynamic activity analysis capability is lost
Solution Approach 1:
The patent implements continuous tracking of animal movement by processing a series of images over time and maintaining movement trajectory data. This continuous analysis enables the system to not only identify animals but also analyze their activities, behaviors, and interactions, transforming static identification into dynamic activity monitoring.
Solution Approach 2:
The system performs preliminary skeleton estimation and key point detection on each frame to establish baseline movement patterns. By pre-processing the movement data and extracting skeletal information, the system prepares the foundation for subsequent activity analysis and behavioral interpretation, enabling versatile applications beyond simple identification.
Data Source
AI summary
A challenge is to provide an art to identify mice as individuals from captured images of one or more mice that are doing activity in a predetermined activity range. An image processing device includes an image acquisition unit, a part extraction unit, and an individual identification unit. The image acquisition unit acquires a captured video of a state in which one or more animals are doing activity in a predetermined activity range. An outline detection unit extracts the outlines of the bodies of the one or more animals from each of multiple unit images included in the video. The individual identification unit analyzes the multiple outlines detected from each of the multiple unit images by the outline detection unit, in a time-series manner and identifies the one or more animals as individuals in each of the unit images on the basis of the analysis results. Thus, the challenge is solved.