Computer Vision System for Sub-Room Space Use Identification
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Solution Overview
Problem
Current computer vision systems lack the capability to provide identification and quantification of space use at a sub-room level of granularity while maintaining privacy and efficiently processing video data.
Innovation Solution
A computer vision system utilizing a GPU-enabled embedded platform that performs accelerated parallel computations on image frames captured by a camera, employing an open-source fully convoluted neural network (YOLOv2) for object detection and a proximity-based tracking algorithm to anonymously detect and track individuals, with data processing occurring in real-time and only relevant data being stored, allowing for reduced video feed processing and maintaining privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If video streaming is used for continuous monitoring, then real-time detection capability is improved, but privacy concerns and data security risks worsen
Solution Approach 1:
The system extracts only the essential detection information (object presence, location, movement) from the video stream while discarding the actual video content. This is achieved through edge computing that processes video frames locally to generate detection events, which are then transmitted to the cloud without sending the raw video data, thus maintaining real-time detection while protecting privacy.
Solution Approach 2:
The system segments the video processing function into two parts: real-time edge processing for detection and cloud processing for analysis. The edge device handles frame-by-frame detection locally to maintain real-time capability, while the cloud receives only structured detection data rather than continuous video streams, reducing privacy risks.
2Measurement precision
If complete video feed is processed and stored, then detection accuracy is improved, but data storage requirements and processing complexity worsen
Solution Approach 1:
The system extracts only relevant detection data (object coordinates, movement vectors, detection timestamps) from the video frames rather than storing complete video feeds. The edge computing device processes each frame to extract essential information, which is then stored in a structured format in the cloud, maintaining detection accuracy while minimizing storage requirements.
Solution Approach 2:
The system performs partial processing by analyzing only specific regions of interest in video frames and extracting only necessary detection parameters. Rather than processing and storing all video data, it focuses computation on detecting object presence and movement, achieving sufficient detection accuracy with minimal data storage.
3Device complexity
If traditional CPU-based processing is used, then system simplicity is maintained, but processing speed and real-time performance worsen
Solution Approach 1:
The system replaces traditional CPU-based sequential processing with GPU-based parallel processing for video frame analysis. The edge computing device utilizes the parallel computing power of GPUs to simultaneously process multiple pixels and detection features across video frames, dramatically improving processing speed while maintaining real-time performance requirements.
Solution Approach 2:
The system dynamically adapts the processing architecture by using GPU acceleration for computationally intensive detection tasks while relying on CPU for control and data management functions. This dynamic allocation of processing resources optimizes both speed and system simplicity.
4Measurement precision
If detailed tracking information is collected, then space use analysis precision is improved, but data processing complexity and computational requirements worsen
Solution Approach 1:
The system segments tracking data into essential spatial parameters (x, y coordinates, movement direction, dwell time) rather than collecting complete trajectory information. The edge device extracts only the segmentation-relevant parameters needed for space use analysis, reducing data processing complexity while maintaining sufficient precision for identifying common areas of dwell and movement pathways.
Solution Approach 2:
The system applies different levels of tracking detail to different spatial zones based on their importance for space use analysis. High-traffic or strategically important areas receive more detailed tracking parameters, while less critical areas use simplified tracking, optimizing the balance between analysis precision and processing complexity.
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 identification and quantification of space use at a sub room level of granularity.