Single Camera Crowd Segmentation via Foot-to-Head Plane
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Solution Overview
Problem
Existing methods for segmenting crowds into individuals in crowded environments are inefficient, particularly when individuals are in groups with freedom of movement, as they require multiple cameras, extensive training data, and are not suitable for tracking in real-time, especially in scenarios like surveillance and mass experimentation.
Innovation Solution
A system utilizing a single image capturing device and an image processing system that employs a foot-to-head plane technique for crowd segmentation, including a foreground estimation module, tracking module, and crowd segmentation module, which calibrates and processes images to separate individuals from groups without the need for multiple frames of reference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used to segment crowds into individuals, then the accuracy of individual detection is improved, but the device complexity and installation cost increase
Solution Approach 1:
The patent segments the crowd scene into multiple depth layers using a single camera by analyzing occlusion relationships and spatial positions of individuals. This creates virtual depth information without requiring multiple physical cameras, resolving the contradiction between detection accuracy and device complexity
Solution Approach 2:
The patent introduces a depth dimension through computational analysis of 2D image data from a single camera. By inferring spatial relationships and depth ordering from occlusion patterns, the system creates a 3D-like understanding of crowd structure without adding physical camera dimensions
2Measurement precision
If model-based object detection with learned appearance models is used, then individual segmentation is achieved, but the training data requirements and system complexity increase
Solution Approach 1:
The system performs self-calibration by automatically learning appearance models and spatial relationships from the video data itself without requiring external training datasets. The algorithm adapts to the specific scene and individuals present, eliminating the need for separate training phases and reducing setup complexity
3Measurement precision
If conventional crowd segmentation methods are used, then individual detection is possible in constrained settings, but the methods fail when individuals have freedom of movement and are in groups
Solution Approach 1:
The patent employs dynamic tracking that continuously updates individual models as people move and change appearance. The system adapts to movement by maintaining temporal consistency and updating appearance models frame-by-frame, allowing accurate tracking of individuals with freedom of movement rather than requiring static constrained positions
4Measurement precision
If multiple frames of reference from multiple cameras are used, then crowd segmentation into individuals is achieved, but the cost and operational complexity increase
Solution Approach 1:
The patent creates virtual copies of depth and spatial information through computational processing of a single camera's 2D image data. By synthesizing depth maps and spatial relationships from monocular cues like occlusion and perspective, the system replicates the functionality of multiple cameras without the associated costs and operational complexities
Data Source
AI summary
A system for detecting and counting individuals in a stationary or moving crowd based on a digital or digitized image captured from a single camera. Initial information is assumed based on a foot-to-head plane homology, where a geometric construct is developed to best enclose image features with a high probability of being an individual within a crowd. These geometric constructs are then subjected to further probabilistic analysis to determine individuals. The vector track of each individual is determined to validate the determination of a group of features as individuals, thereby compensating for occlusion of an individual within any given frame image. A virtual gate is then employed to count the individuals moving past the virtual gate. Also, by weighting portions of the individual, events near the gate can be predicted.