Object Counting via Spatio-Temporal Image Analysis
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Solution Overview
Problem
Conventional image processing systems face challenges in real-time object detection and classification due to excessive image data processing requirements, particularly in applications like traffic monitoring, where efficient and reliable solutions are needed to identify and count objects of interest.
Innovation Solution
A method involving an imaging device that extracts a line of pixels from predefined zones perpendicular to the expected direction of travel, constructs a spatio-temporal image, and uses edge detection and cluster analysis to classify objects, allowing for real-time processing without requiring contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If general subtraction methods are used to process images for object detection, then object detection capability is achieved, but processing time and computational load increase excessively
Solution Approach 1:
The patent divides the image processing task into segments: instead of processing entire images, it extracts only specific lines of pixels from predefined zones where objects are expected to appear. This segmentation reduces the data volume significantly while maintaining detection capability for objects in monitored areas.
Solution Approach 2:
The patent extracts only the necessary portion of image data - specifically lines of pixels from predefined zones - rather than processing complete images. This extraction approach removes unnecessary data processing steps while preserving the ability to detect and classify objects of interest.
2Measurement precision
If complete image data is processed for accurate object classification, then classification accuracy is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent applies local quality by focusing processing resources on specific regions (predefined zones) where objects are expected, rather than uniformly processing entire images. This allows accurate classification within monitored areas while reducing overall computational complexity.
Solution Approach 2:
The patent uses partial action by processing only the minimum necessary image data - lines from predefined zones - rather than complete images. This partial processing approach maintains sufficient accuracy for classification while significantly reducing computational burden.
3Speed
If real-time processing is implemented for traffic monitoring applications, then response speed is improved, but processing reliability may deteriorate due to reduced processing time
Solution Approach 1:
The patent performs preliminary actions by predefining zones and target lines before object detection begins. This preparation allows the system to process only relevant data in real-time without compromising detection reliability, as the monitoring areas are established in advance based on expected object locations.
Data Source
AI summary
Systems and methods according to one or more embodiments are provided for classifying and counting objects that pass through monitored zones in a field of view of an imaging device. An imaging device receives a series of images of a target scene having at least one monitored zone. An image processor extracts a target line of image values from each image, and adds the target line to a first spatio-temporal image. An edge detector and a cluster analysis module analyzes the first spatio-temporal image to identify objects associated with the at least one zone. An object classifier classifies in object and stores tracking information.