Computer Vision System with Real-Time Privacy Coaching
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Solution Overview
Problem
Current computer vision systems lack efficiency in detecting and tracking individuals within a target field while maintaining privacy and security, and they do not provide real-time performance improvements or movement pathway efficiency.
Innovation Solution
A computer vision system that uses a processor to perform accelerated parallel computations on image frames from a camera, employing an open-source neural network like YOLOv2 for object detection and a proximity-based tracking algorithm, which anonymously detects and tracks people, processes images in real-time, and stores only relevant data, allowing for reduced video feeds and maintaining privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If video is streamed continuously for detection and tracking, then real-time monitoring capability is improved, but privacy is compromised and data storage requirements increase
Solution Approach 1:
The system extracts only the essential information (presence, location, and movement of individuals) from the video stream while discarding the actual video content. This is achieved through edge detection algorithms that identify human figures and track their positions, then stores only the extracted data rather than the complete video feed, thereby maintaining real-time monitoring capability while protecting privacy.
Solution Approach 2:
The system applies different processing quality levels to different aspects of the video data. Full-resolution processing is applied only to regions containing individuals for accurate detection and tracking, while other regions receive minimal or no processing. This localized approach maintains detection accuracy where needed while reducing overall data processing and storage requirements.
2Measurement precision
If complete video feeds are stored for analysis, then detection accuracy is improved, but data storage needs increase and processing complexity increases
Solution Approach 1:
The system extracts only the essential information (presence, location, and movement of individuals) from the video stream while discarding the actual video content. This is achieved through edge detection algorithms that identify human figures and track their positions, then stores only the extracted data rather than the complete video feed, thereby maintaining real-time monitoring capability while protecting privacy.
Solution Approach 2:
The video stream is segmented into discrete detectable elements (individuals, their positions, and movement vectors) rather than processing it as continuous video data. This segmentation allows the system to store and analyze only the essential tracking information without retaining the full video footage, significantly reducing storage requirements while maintaining detection accuracy.
3Device complexity
If traditional sequential processing is used for image frames, then system complexity is reduced, but processing speed and real-time performance deteriorate
Solution Approach 1:
The processing system is segmented into multiple parallel processing streams that operate simultaneously on different aspects of the video data (e.g., one stream for detection, another for tracking, another for analysis). This parallel architecture increases processing speed and real-time performance while maintaining manageable system complexity through modular design.
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 provides physical security coaching.