Computer Vision Movement Pathway Identification
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Solution Overview
Problem
Current computer vision systems lack efficient identification of common movement pathways within a space while maintaining privacy and security, and they often rely on unmodified video streaming which raises privacy concerns.
Innovation Solution
A computer vision system utilizing a USB camera connected via an external USB expansion hub, a cellular-to-Ethernet router, and an open-source YOLOv2 neural network for real-time object detection, combined with a proximity-based tracking algorithm to anonymously detect and track people, and a user interface with status LEDs for system monitoring, processes images in real-time without storing video, storing only relevant data for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If video streaming is used to detect people and relay coordinates, then real-time detection capability is improved, but privacy security deteriorates due to unmodified video data transmission
Solution Approach 1:
The system extracts only the necessary detection data (person coordinates, detection timestamp) from the video stream while discarding the actual video content. This extraction is performed by processing individual frames through the YOLOv2 neural network and tracking algorithm, then transmitting only the extracted metadata to the server, thereby maintaining real-time detection capability while eliminating privacy risks associated with video streaming
Solution Approach 2:
The system creates a simplified representation (copy) of the detection data that contains only the essential information needed for analysis. Instead of transmitting the original video stream, the system copies and transmits only the extracted detection parameters (person ID, coordinates, timestamp), which preserves the functional capability for real-time monitoring while removing sensitive visual data
2Measurement precision
If video streaming is used for people detection, then detection accuracy is improved, but data storage requirements increase
Solution Approach 1:
The system extracts only the essential detection parameters (person coordinates, detection timestamp, tracking ID) from the video stream and stores only this condensed data. The actual video frames are not stored, reducing data storage requirements while maintaining detection accuracy through the YOLOv2 neural network and proximity-based tracking algorithm
Solution Approach 2:
The system discards the video data after extraction and recovers only the necessary detection information for storage and analysis. The YOLOv2 model processes video frames to extract detection parameters, which are then stored and used for pathway analysis, while the original video content is discarded to minimize storage needs
3Measurement precision
If camera parameters are provided for each image, then detection precision is improved, but system complexity increases
Solution Approach 1:
The system uses a universal camera rig configuration with a fixed reference camera and secondary cameras at known relative positions. This multi-functional setup allows the system to handle multiple detection tasks (person detection, tracking, pathway analysis) using a standardized camera rig model, reducing system complexity while maintaining detection precision through the established camera calibration and transformation matrices
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 of common movement pathways within a space.