Surveillance Camera Fusion for Crowd Attribute Estimation
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Solution Overview
Problem
Existing video surveillance techniques fail to accurately determine the number and type of individuals in crowded areas, as they treat crowds as homogeneous collections, making it difficult to distinguish between different people and estimate attribute information such as age and sex.
Innovation Solution
An information processing apparatus that combines data from fixed and moving cameras to calculate the flow and attribute distribution of objects, allowing for the estimation of attribute distribution in areas not directly captured by the moving camera using the flow data from the fixed camera.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a fixed camera is used to capture a wide area, then the coverage area is improved, but the ability to recognize individual attributes (age, sex, etc.) deteriorates
Solution Approach 1:
The system divides the surveillance area into multiple zones and uses both fixed and moving cameras to cover different regions. The moving camera focuses on specific areas to capture detailed attribute information, while the fixed camera provides overall coverage, segmenting the monitoring functions to resolve the contradiction between wide coverage and detailed recognition.
Solution Approach 2:
The system merges the data from fixed cameras (providing wide-area coverage and flow information) and moving cameras (providing detailed attribute information) to create a comprehensive surveillance system. By combining the strengths of both camera types, the system achieves both wide coverage and accurate attribute recognition simultaneously.
2Measurement precision
If a moving camera is used to capture detailed attributes, then the attribute recognition accuracy is improved, but the coverage area deteriorates
Solution Approach 1:
The fixed camera acts as an intermediary that provides flow information and positional data about objects moving through the surveillance area. This flow information mediates between the limited coverage of the moving camera and the need for wide-area monitoring, allowing the system to track objects across the entire area while the moving camera focuses on capturing detailed attributes in its immediate vicinity.
3Device complexity
If crowd members are treated as a homogeneous collection, then the processing complexity is reduced, but the ability to determine individual attributes deteriorates
Solution Approach 1:
The system applies different processing qualities to different objects based on their importance and the available data. For objects captured by the moving camera, detailed attribute analysis is performed. For objects only captured by fixed cameras, flow-based estimation is used. This local differentiation of processing quality allows the system to maintain low overall complexity while preserving critical attribute information where possible.
4Measurement precision
If individual separation of people is attempted in crowded areas, then the attribute recognition accuracy is improved, but the detection difficulty increases
Solution Approach 1:
The system dynamically adjusts its detection strategy based on crowd density and object movement. In crowded areas where individual separation is difficult, the system uses flow information from fixed cameras to track objects over time and estimates attributes based on movement patterns. In less crowded areas, the system switches to detailed image analysis by the moving camera for accurate attribute recognition. This dynamic adaptation resolves the contradiction between accuracy and detection difficulty.
Data Source
AI summary
An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).