Crowd Counting via Image Projection and Kalman Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for crowd counting in traffic control are inefficient due to scalability and accuracy issues, particularly in real-time monitoring and tracking of pedestrians and groups, which are costly and prone to errors from occlusions and misidentification of objects.
Innovation Solution
A system using extended Kalman filters and camera calibration data to track individuals and groups by segmenting foreground regions, estimating people counts based on area measurements and shape models, and mitigating occlusions by maintaining a history of estimates and treating groups as cohesive entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used for crowd counting, then accuracy can be maintained through direct observation, but the method is exceedingly costly and too slow for real-time applications
Solution Approach 1:
The patent replaces manual mechanical counting methods with an automated computer vision system that uses image processing algorithms to detect, segment, and count crowd members. This substitution enables real-time automated counting while maintaining accuracy through sophisticated image analysis techniques.
2Extent of automation
If previous processor-based methods are used for crowd counting, then automation is achieved, but scalability and accuracy problems persist
Solution Approach 1:
The patent segments the crowd into individual detectable units by identifying contours and bounding boxes around each person in the image. This segmentation approach allows the system to count individual members even in dense crowds, improving accuracy while maintaining automation. The segmentation process handles overlapping and occluded individuals through advanced image processing.
Solution Approach 2:
The patent transitions from two-dimensional image data to three-dimensional crowd structure analysis by inferring spatial relationships and depth information. This dimensional enhancement allows for more accurate counting by distinguishing between individuals at different depths and resolving occlusions through geometric reasoning.
3Device complexity
If traditional crowd counting methods are used, then simplicity is maintained, but the system suffers from errors due to occlusions and misidentification of objects
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously refines its crowd count estimates by analyzing multiple image frames and adjusting for detected occlusions. The feedback loop identifies misidentified objects and corrects counting errors, improving reliability while maintaining reasonable system complexity through iterative optimization.
Solution Approach 2:
The patent performs preliminary actions by pre-processing images to enhance crowd member visibility before counting. This includes applying filters to reduce noise, enhancing contrast to separate individuals, and predicting crowd member positions based on motion patterns. These preliminary steps reduce the impact of occlusions and misidentifications before the actual counting process.
Data Source
AI summary
This document discusses, among other things, methods and systems for determining the number of members in a group as well as changes over a period of time. Using an image of the scene, an overlap area is calculated by projecting portions of the image onto spaced apart and parallel planes. A filter correlates the overlap area to the number of members.