Multi-View 3D Density Mapping for Accurate Crowd Counting
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Solution Overview
Problem
Existing crowd counting methods based on single or multiple views face challenges such as erroneous calculations due to crowding and occlusion, and require cumbersome camera recalibration and heavy computational burden when camera positions change.
Innovation Solution
A system comprising multiple monitor devices capturing images from different views, generating 2D density maps, and a processing device creating a 3D density map to calculate the number of objects, which allows for accurate counting without recalibration and shares computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple views are used for crowd counting, then measurement precision is improved, but device complexity and ease of operation deteriorate due to camera calibration requirements
Solution Approach 1:
The system performs self-calibration by automatically determining camera positions and parameters through image processing and feature matching, eliminating the need for manual recalibration when cameras change positions. The system serves itself by autonomously adapting to camera movements without external intervention.
Solution Approach 2:
The system dynamically updates camera parameters and positions based on detected changes in the imaging environment. When cameras move or reposition, the system automatically adjusts calibration parameters to maintain accurate crowd counting without requiring manual recalibration procedures.
2Measurement precision
If multiple views are used for crowd counting, then measurement precision is improved, but computational burden increases due to data fusion requirements
Solution Approach 1:
The system divides the crowd counting task into independent segments, with each camera processing its own images to generate local density maps. This segmentation allows parallel processing across multiple cameras, reducing the computational burden on any single device while maintaining overall accuracy through aggregation of results.
Solution Approach 2:
The system transforms 2D image data from multiple cameras into a unified 3D spatial representation. By projecting and fusing density maps across different viewing angles into a three-dimensional model, the system achieves more accurate crowd counting while distributing computational tasks across dimensional transformations rather than requiring complex multi-view fusion algorithms.
3Device complexity
If single view is used for crowd counting, then device complexity is reduced, but measurement precision deteriorates due to crowding and occlusion
Solution Approach 1:
The system merges multiple camera views into a unified three-dimensional spatial model. By combining information from several perspectives, the system overcomes the limitations of single-view methods such as crowding and occlusion, achieving improved accuracy while maintaining manageable system complexity through integrated processing.
Solution Approach 2:
The system introduces a three-dimensional density map as an intermediary representation that bridges multiple camera views. This 3D model serves as a mediator that integrates information from different perspectives, allowing the system to resolve ambiguities caused by occlusion and crowding that would otherwise limit single-view accuracy.
Data Source
AI summary
The present disclosure provides area information estimation method and system. The area information estimation system includes a processing device and a plurality of monitor devices. The area information estimation method includes: by the plurality of monitor devices, capturing a plurality of images of an area from different views; by the plurality of monitor devices, generating a plurality of two-dimensional (2D) density maps of at least one target object in the area according to the plurality of images; by the processing device, generating a three-dimensional (3D) density map according to the plurality of 2D density maps; and by the processing device, calculating a number of the at least one target object according to the 3D density map.