Multi-Camera Detection Device Using Shared Memory for Feature Unification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing monitoring systems struggle to efficiently recognize and re-recognize objects and persons across multiple cameras with varying perspectives, leading to inconsistencies and inefficiencies in feature determination.
Innovation Solution
A detection device comprising multiple analysis modules, each equipped with a neural network, that process monitoring data from cameras with different perspectives, and a shared memory module to unify feature determination and store perspective-independent features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras with different perspectives are used to monitor the area, then the recognition accuracy and coverage are improved, but the system complexity and data processing burden increase
Solution Approach 1:
The system divides the monitoring task into multiple independent analysis modules, each responsible for processing data from specific cameras or camera groups. Each module contains its own neural network that independently extracts features from its assigned cameras, allowing parallel processing and reducing the complexity burden on any single component while maintaining comprehensive multi-perspective analysis.
Solution Approach 2:
The shared memory module serves multiple functions: it stores features from all cameras, provides data to all analysis modules, and enables cross-module information exchange. This universal data repository allows the system to handle diverse camera perspectives without requiring separate processing paths for each camera, simplifying the overall system architecture while maintaining recognition accuracy.
2Productivity
If separate analysis modules are used for each camera, then the processing efficiency and modularity are improved, but the memory usage and data redundancy increase
Solution Approach 1:
The system merges the memory resources of multiple analysis modules into a single shared memory module. Instead of each analysis module maintaining separate memory spaces for storing camera features, all modules access a common memory repository. This consolidation eliminates redundant storage across modules while preserving the parallel processing capabilities that drive high productivity.
Solution Approach 2:
The shared memory module acts as an intermediary between multiple analysis modules and the central processing system. It receives features from various cameras, standardizes the data format, and distributes relevant information to appropriate modules. This intermediary approach enables efficient memory utilization by preventing direct duplicate storage while maintaining the modular architecture's processing efficiency.
3Stability of the object's composition
If a shared memory module is implemented, then the feature unification and data consistency are improved, but the system complexity and coordination overhead increase
Solution Approach 1:
The shared memory module enforces homogeneous data structures and access protocols across all analysis modules. By standardizing how features are stored, retrieved, and formatted in the shared memory space, the system achieves data consistency without requiring complex coordination logic. Each module interacts with the shared memory in the same uniform way, simplifying the coordination overhead.
4Measurement precision
If neural networks are used in each analysis module, then the feature determination accuracy is improved, but the computational power and energy consumption increase
Solution Approach 1:
The computational workload is segmented across multiple independent analysis modules, each running its own neural network. Instead of one large neural network processing all camera data sequentially, multiple smaller neural networks process data from different cameras in parallel. This segmentation maintains high feature determination accuracy while distributing energy consumption across multiple units, reducing the peak power requirements and enabling more efficient resource utilization.
Data Source
AI summary
Detection device 1 for recognizing an object and/or a person 3, 3a, b, c in a monitoring area 2 monitored with a plurality of cameras 4, 4a, b, c, having a plurality of analysis modules 6, 6a, b, c, wherein each analysis module 6, 6a, b, c in each case preferably has and/or forms a neural network, wherein the analysis modules 6, 6a, b, c in each case have a data connection to at least one camera 4, 4a, b, c, wherein monitoring data from the connected cameras 4, 4a, b, c are provided in each case to the analysis modules 6, 6a, b, c, having a shared memory module 7, wherein the analysis modules 6, 6a, b, c have a data connection to the shared memory module 7, wherein the analysis modules 6, 6a, b, c are designed in each case to retrieve memory data associated with the monitoring data from the shared memory module 7, wherein the analysis modules 6, 6a, b, c are designed in each case to determine features, object features and/or person features on the basis of the monitoring data and the memory data.