Cross-Sensor Object-Attribute Analysis for Multi-Camera Tracking
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Solution Overview
Problem
Conventional security camera systems in buildings or stores lack collaborative processing functions, making it difficult for security guards to focus on multiple screens simultaneously and identify abnormal events or suspicious persons.
Innovation Solution
A cross-sensor object-attribute analysis method using an edge computing architecture with multiple image sensing devices to cooperatively detect objects, process images in real-time, and generate attribute vectors for object recognition and tracking, employing AI modules for identification and trajectory analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple cameras are installed to cover the space, then the coverage area is improved, but the complexity of monitoring and processing increases
Solution Approach 1:
The patent merges data from multiple image sensing devices through collaborative processing. The main information processing device aggregates images and attribute vectors from multiple cameras, performs joint analysis, and generates unified detection results, reducing the complexity of monitoring multiple screens separately.
Solution Approach 2:
The patent introduces an intermediary processing layer with information processing units in each camera and a central main processing device. This intermediary architecture automatically performs object detection, attribute vector generation, and trajectory tracking, serving as a mediator between raw camera data and security guard interpretation.
2Area of stationary object
If multiple screens are set up for monitoring, then the monitoring coverage is improved, but the ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically detecting objects, generating attribute vectors, and tracking trajectories without requiring security guards to manually analyze multiple screens. The intelligent processing units autonomously identify suspicious persons and abnormal events, reducing the operational burden on monitoring personnel.
Solution Approach 2:
The patent replaces the mechanical process of manual screen monitoring with intelligent algorithms. AI-based object detection and attribute analysis automatically process images from multiple cameras, substituting human effort with automated computational analysis to improve ease of operation.
3Measurement precision
If collaborative processing is implemented, then the identification accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent segments the collaborative processing system into modular components: information processing units in each camera for local preprocessing, attribute vector generation, and a central main processing device for integrated analysis. This segmentation improves identification accuracy through collaborative processing while managing complexity through modular architecture.
4Loss of time
If real-time processing is performed, then the response time is improved, but the energy consumption increases
Solution Approach 1:
The patent performs preliminary action by generating attribute vectors and performing object detection at the edge devices (information processing units in each camera) before transmitting data to the central processing device. This preliminary processing reduces the amount of data requiring real-time transmission and centralized processing, achieving real-time response while reducing overall energy consumption.
Data Source
AI summary
A cross-sensor object-attribute analysis method for detecting at least one object in a space by using cooperation of a plurality of image sensing devices, the method including: the image sensing devices sending raw data or attribute vector data of sensed multiple images to a main information processing device, where the raw data and attribute vector data all correspond to a time record; and the main information processing device generating one or more of the attribute vectors according to the raw data of each of the images and using each of the one or more of the attribute vectors to correspond to one of the at least one object, or the main information processing device directly using the attribute vector data to correspond to the at least one object.