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, health, safety, evacuation routes, and responder wayfinding while maintaining privacy and security.
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.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If video is streamed for continuous monitoring, then real-time detection capability is improved, but privacy and security are compromised
Solution Approach 1:
The system extracts only the essential detection data (animal presence, wellness indicators, habitat conditions) from the video stream and transmits only this processed information to the server, rather than streaming the entire video feed. This extraction approach maintains real-time detection capabilities while minimizing privacy intrusion by removing unnecessary visual data.
Solution Approach 2:
The local device acts as an intermediary that processes video data locally before transmission to the server. The intermediary performs object detection and extracts relevant features locally, then sends only the extracted data (not the original video) to the server, thus mediating between real-time monitoring needs and privacy protection requirements.
2Measurement precision
If complete video data is transmitted to server, then detection accuracy is improved, but data transmission bandwidth and processing load increase
Solution Approach 1:
The system extracts only the essential detection data (animal presence, wellness indicators, habitat conditions) from the video stream and transmits only this processed information to the server, rather than streaming the entire video feed. This extraction approach maintains real-time detection capabilities while minimizing privacy intrusion by removing unnecessary visual data.
Solution Approach 2:
The system performs preliminary object detection and data extraction locally on the device before transmission to the server. By conducting preliminary processing steps locally, the system reduces the volume of data that needs to be transmitted while ensuring that detection accuracy is maintained through local analysis of the video content.
3Area of stationary object
If multiple cameras are deployed for comprehensive coverage, then detection coverage is improved, but system complexity increases
Solution Approach 1:
The system is designed with universal processing capabilities that can handle input from multiple cameras with different configurations (including fisheye lenses). The detection algorithms and processing pipeline are unified and can process data from any number of cameras, allowing the system to scale from single-camera to multi-camera deployments without requiring separate processing systems for each camera.
Solution Approach 2:
The system divides the monitoring area into multiple zones or regions, with each camera responsible for a specific segment. The processing system handles each camera's data independently through modular processing units, then integrates the results. This segmentation approach allows comprehensive coverage while managing complexity through divide-and-conquer architecture.
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.