Hybrid Camera Network for Scalable Observation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current camera networks are inefficient for large spaces due to sensor limitations and require continuous activation, lacking scalable and accurate activity recognition capabilities, especially in complex environments.
Innovation Solution
A hybrid camera network architecture with a master sensor and secondary sensors, where the master sensor monitors the space, detects regions of interest, and activates/deactivates secondary sensors dynamically for precise activity recognition, using a combination of omnidirectional sensors and software modules for efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple sensors are activated continuously to cover large spaces, then observation coverage is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts sensor activation status based on real-time observation needs. The master sensor continuously monitors the space and triggers secondary sensors only when regions of interest are detected, transforming the static continuous activation model into a dynamic on-demand activation model that adapts to changing observation requirements.
Solution Approach 2:
Instead of continuous activation, the system uses periodic monitoring by the master sensor to detect regions of interest, followed by selective activation of secondary sensors only during periods when observation is needed. This periodic action pattern reduces energy consumption while maintaining effective coverage.
2Measurement precision
If all sensors are activated continuously to ensure accurate activity recognition, then recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The sensor network is segmented into a hierarchical structure with one master sensor and multiple secondary sensors. The master sensor handles continuous monitoring and region of interest detection, while secondary sensors are selectively activated only for specific regions and tasks. This segmentation reduces overall system complexity by distributing functions across different sensor levels.
Solution Approach 2:
The master sensor acts as an intermediary between the environment and secondary sensors. It processes raw sensor data, identifies regions of interest, and selectively triggers secondary sensors based on detected activities. This intermediary role simplifies the system by centralizing control logic and reducing direct interactions between multiple secondary sensors.
3Measurement precision
If a calibration step is implemented for camera communication, then coordination accuracy is improved, but setup time increases
Solution Approach 1:
The system implements self-service calibration through the master sensor's central coordination role. The master sensor automatically manages communication and coordination with secondary sensors based on detected regions of interest, eliminating the need for manual pre-calibration of all camera pairs. The system adapts its coordination dynamically based on actual observation needs.
4Loss of energy
If secondary sensors are selectively activated based on regions of interest, then energy efficiency is improved, but detection speed may decrease
Solution Approach 1:
The master sensor performs preliminary monitoring and pre-identifies regions of interest before activating secondary sensors. This preliminary action allows the system to prepare for selective sensor activation in advance, minimizing detection delays while maintaining energy efficiency. The master sensor's continuous monitoring ensures that regions of interest are detected and secondary sensors are triggered promptly.
Data Source
AI summary
A method, a non-transitory computer readable medium, and a system are disclosed for observing one or more subjects. The method includes monitoring a space with at least one master sensor, wherein a plurality of secondary sensors are installed in the space, and wherein a number of the at least one master sensor is less than a number of the plurality of secondary sensors; detecting regions of interest based on input from the at least one master sensor; identifying one or more secondary sensors from the plurality of secondary sensors in the detected regions of interest; and recognizing activities in the detected regions of interest from the one or more secondary sensors.