Real-Time Collision Detection Using Anonymized Video Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer vision systems lack the capability to effectively identify and track collisions between individuals in real-time while maintaining privacy and security, and they often rely on streaming unmodified video, which raises privacy concerns.
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 to anonymously detect and track people within a target field, storing only processed data and not the video feed, and using a cellular-to-ethernet conversion router for connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video is streamed for real-time collision detection, then collision detection capability is improved, but privacy is compromised
Solution Approach 1:
The system extracts only the essential data elements needed for collision detection (person detection, tracking, proximity calculation) from the video stream, processing and analyzing this extracted data locally without transmitting the actual video content. This separates the useful information extraction from the privacy-invasive video streaming, resolving the contradiction between detection capability and privacy protection.
Solution Approach 2:
The system segments the video processing into discrete analytical steps: frame capture, person detection via neural network, tracking algorithm execution, proximity calculation, and collision determination. Each segment processes only necessary data elements, allowing real-time collision detection while maintaining privacy by never consolidating or transmitting identifiable video information.
2Measurement precision
If unmodified video is streamed for analysis, then detection accuracy is improved, but security and privacy compliance deteriorate
Solution Approach 1:
The system performs preliminary processing of video frames immediately upon capture, extracting person detection data and tracking information before any potential transmission or storage. This preliminary extraction of essential metrics occurs locally on the device, ensuring detection accuracy is maintained while privacy-compliant data (non-identifiable analytical results) is what gets stored or transmitted.
Solution Approach 2:
Instead of streaming or storing actual video content, the system creates and uses analytical copies - structured data representations of person positions, trajectories, and proximity relationships. These copies contain all necessary information for accurate collision detection while being inherently privacy-compliant as they lack identifiable visual information.
3Speed
If real-time processing is implemented, then collision identification speed is improved, but computational resource requirements increase
Solution Approach 1:
The system applies partial processing to each video frame - using optimized neural network inference and tracking algorithms that process only the essential elements needed for collision detection. Rather than analyzing every pixel or detail in each frame, the system focuses computational resources on detecting and tracking persons and calculating their proximity, achieving real-time performance with reduced energy consumption.
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 an identification of where collisions regularly occur.