Semantic Signal Segment Sharing for Multi-Sensor Entity Tracking
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Solution Overview
Problem
Existing surveillance systems struggle to efficiently share and process signal segments, particularly in complex environments with multiple sensors, lacking the ability to accurately identify and track physical entities and adapt to changing viewpoints.
Innovation Solution
A system that utilizes a semantic understanding of signal segments to detect and extract portions of interest, enabling the sharing of these segments with recipients, including transformed forms such as narrations, while maintaining flexibility in representation and adaptability to changing sensor viewpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional surveillance systems process and share signal segments from multiple sensors, then system coverage and monitoring capability are improved, but data transmission overhead and processing complexity increase significantly
Solution Approach 1:
The patent segments signal data at the granularity of physical entities rather than transmitting complete sensor signal segments. The system identifies and extracts only the portions of signals that contain information about specific physical entities of interest, thereby dividing the data transmission task into smaller, more manageable units that reduce overall complexity while maintaining surveillance coverage.
Solution Approach 2:
The system extracts and transmits only the relevant portions of signal segments that contain information about selected physical entities, rather than transmitting complete sensor data. This extraction approach removes unnecessary data from the transmission pipeline, reducing processing complexity and bandwidth requirements while preserving essential surveillance information.
2Measurement precision
If complete signal segments are transmitted to enable accurate entity identification, then recognition accuracy is improved, but data transmission volume and network bandwidth consumption increase
Solution Approach 1:
The system extracts only the essential portions of signal segments that contain information about physical entities of interest. By identifying and transmitting only these relevant segments rather than complete sensor data, the system maintains entity recognition accuracy while significantly reducing the volume of data transmitted across the network.
Solution Approach 2:
The patent applies different levels of data transmission quality based on the specific needs of different physical entities and their importance. Rather than uniformly transmitting all signal data at the same level, the system selectively transmits data with appropriate detail and resolution for each entity, optimizing bandwidth usage while preserving recognition accuracy for critical entities.
3Speed
If signal segments are processed in real-time to track moving entities, then tracking responsiveness is improved, but computational load and processing time increase
Solution Approach 1:
The system extracts and transmits only the essential information about physical entities from complete signal segments, reducing the computational load required for real-time processing. By focusing computational resources on extracting and processing only the relevant portions of data rather than analyzing complete sensor signals, the system achieves responsive tracking with lower energy consumption.
4Reliability
If multiple sensors are deployed to improve detection accuracy in complex environments, then detection capability is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent merges data from multiple sensors at the level of physical entity identification rather than coordinating complete sensor signals. By combining information about the same physical entities detected by different sensors and transmitting unified entity-based data segments, the system improves detection capability while reducing the coordination complexity that would arise from managing multiple independent sensor streams.
Data Source
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AI summary
The sharing of signal segments using a semantic understanding of what is included within the signal segment. Upon selection of a physical entity (or portion thereof) that is rendered within a signal segment, the system then extracts portion(s) of signal segment(s) in which that selected physical entity appears. A representation of the extracted signal segment portion are then dispatched to recipients. Thus, portions of signal segments may be shared at the granularity of the content of what the signal segment depicts. There is wide flexibility in what representations of those signal segment portion(s) are shared.