Computer Vision System for Real-Time Animal Wellness Detection
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Solution Overview
Problem
Current computer vision systems lack the capability to effectively detect and track animal wellness, habitat, intervention design, and safety while maintaining privacy and security, and they do not efficiently process video streams to extract relevant data for real-time analysis.
Innovation Solution
A computer vision system that captures image frames, performs accelerated parallel computations, and uses an open-source neural network like YOLOv2 for object detection, combined with proximity-based tracking, to anonymously detect and track individuals within a target field, processing data in real-time and storing only relevant information, without streaming video, thus maintaining privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video streaming is used for real-time analysis, then detection and tracking capability is improved, but privacy and security are compromised
Solution Approach 1:
The patent extracts only the essential detection and tracking information from video data while removing the actual video content. The system processes video frames to extract object detection results, tracking data, and analytical information, then discards the original video streams. This allows real-time analysis capability while preventing privacy breaches since the sensitive video content never leaves the local processing environment.
Solution Approach 2:
The patent introduces an intermediary processing layer between video capture and analysis. Instead of directly streaming video data, the system uses local processing units to intercept video frames, extract relevant information through detection algorithms, and generate anonymized data outputs. This intermediary layer acts as a buffer that enables analytical functionality while blocking the transmission of sensitive visual information.
2Measurement precision
If complete video streams are processed, then analysis accuracy is improved, but computational load and data storage requirements increase
Solution Approach 1:
The patent extracts only the necessary visual information needed for detection and tracking tasks from complete video streams. Instead of processing entire video frames for storage and transmission, the system extracts key features such as object boundaries, motion vectors, and detection confidence scores. This extraction approach maintains analysis accuracy for the specific tasks while dramatically reducing computational load and data handling requirements.
Solution Approach 2:
The patent applies partial processing by focusing computational resources only on regions of interest within video frames. The system identifies areas containing animals or relevant objects and concentrates processing power on those specific regions rather than analyzing every pixel in the entire frame. This selective processing approach achieves sufficient detection accuracy while minimizing overall computational expenditure.
Data Source
AI summary
A computer vision system includes a camera that captures a plurality of image frames in a target field. A user interface is coupled to the camera. The user interface is configured to perform accelerated parallel computations in real-time on the plurality of image frames acquired by the camera. The system detects and tracks animal wellness and habitat/intervention design.