Scanning-Based Video Analysis for Real-Time Object Detection
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Solution Overview
Problem
Traditional machine learning models for analyzing moving objects in videos are resource-intensive, prone to errors, and require complex tracking systems, making them inefficient for real-time analysis, especially in scenarios like counting objects or detecting tailgating through gated entryways.
Innovation Solution
A scanning-based video analysis system generates a composite image by stitching pixels from a region of interest over time, allowing for condensed spatial and historical representation of moving objects, which can be processed more efficiently using a lightweight ML model, reducing computational resources and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning models are used for analyzing moving objects in videos, then detection capability is achieved, but computational resources and system complexity increase significantly
Solution Approach 1:
The patent segments the video analysis task into two distinct stages: (1) generating a composite image that condenses temporal information from multiple video frames into a single spatial representation, and (2) applying a lightweight machine learning model to detect moving objects in the composite image. This segmentation allows the complex temporal analysis to be separated from the detection task, enabling the use of simpler models while maintaining detection capability.
Solution Approach 2:
The patent transforms the temporal dimension of video data into a spatial dimension by creating a composite image where the vertical axis represents time and the horizontal axis represents spatial position. This dimensionality change allows temporal sequences of object movements to be visualized and analyzed as spatial patterns in a single image, enabling lightweight models to process temporal information effectively.
2Productivity
If traditional machine learning models process video frames in real-time, then analysis speed is maintained, but computational resources are excessively consumed
Solution Approach 1:
The patent merges multiple video frames captured over a time period into a single composite image by stitching pixels together. This consolidation reduces the data volume from numerous individual frames to one composite representation, significantly decreasing the computational resources required for processing while maintaining the ability to analyze object movements and patterns.
Solution Approach 2:
The patent creates a simplified copy of the video data in the form of a composite image that preserves essential movement information while reducing complexity. This composite copy can be processed by lightweight machine learning models instead of the original high-resolution video frames, reducing energy consumption while maintaining analysis capability.
3Measurement precision
If complex tracking systems are implemented for accurate object tracking, then tracking precision is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent achieves accurate tracking by transforming temporal tracking information into a spatial representation in the composite image. The vertical position of objects in the composite image corresponds to time, allowing tracking precision to be maintained while using simpler models that analyze spatial patterns rather than implementing complex temporal tracking algorithms.
Data Source
AI summary
Various embodiments described herein provide for analysis of a video using a scanning technique. According to some embodiments, a video is analyzed by scanning a region of interest in a series of frames of the video, and generating a composite image based on the pixels captured by the scanning operation.