Edge Object Detection for Real-Time Tracking and Privacy
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Solution Overview
Problem
Conventional digital video monitoring systems require significant manual review and bandwidth for analysis, leading to high costs and delayed real-time responses, and often violate privacy by transmitting video data for offline analysis, which is not feasible for real-time actions.
Innovation Solution
Implementing object detection devices that capture and analyze image data locally, using background subtraction and computer vision algorithms to identify and track objects of interest in real-time, while minimizing data transmission and ensuring privacy by not sending personal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video data is transmitted to data center for analysis, then detection accuracy is improved, but bandwidth consumption increases significantly
Solution Approach 1:
The patent extracts and processes only the essential features and metadata from video data locally at the edge device, rather than transmitting the complete video stream to the data center. This extraction approach maintains detection accuracy by preserving key object characteristics while dramatically reducing bandwidth consumption by sending only processed results and essential metadata to the cloud.
Solution Approach 2:
The system segments the video analysis function into two parts: local edge processing for feature extraction and object detection, and cloud-based processing for result aggregation and model updates. This segmentation allows accurate local detection with minimal bandwidth usage, as only detection results and metadata need transmission rather than raw video data.
2Measurement precision
If manual review is performed for video monitoring, then detection accuracy is improved, but operational cost increases
Solution Approach 1:
The system implements self-service automated object detection using machine learning models deployed at the edge device. The model automatically detects and classifies objects in video streams without requiring manual review, thereby maintaining high detection accuracy while eliminating the operational costs associated with human reviewers. The system serves itself by performing autonomous analysis and generating detection results.
Solution Approach 2:
The patent replaces the mechanical system of manual human review with an automated computer vision system using machine learning models. This substitution maintains or improves detection accuracy while significantly reducing operational costs by eliminating the need for human operators to manually review video footage.
3Adaptability or versatility
If video data is transmitted for offline analysis, then comprehensive processing is achieved, but real-time response capability is lost
Solution Approach 1:
The system performs preliminary object detection and feature extraction actions locally at the edge device before any cloud communication occurs. This preliminary processing enables real-time response capabilities by completing critical detection functions using local resources, while comprehensive processing is achieved through subsequent cloud-based model updates and result aggregation without requiring offline analysis of complete video streams.
Solution Approach 2:
The system dynamically adjusts its processing mode by performing real-time detection locally at the edge and only transmitting essential data to the cloud for comprehensive processing. This dynamic approach maintains real-time response capability for time-critical detections while still achieving comprehensive analysis through cloud connectivity when available, adapting to varying network conditions and processing requirements.
Data Source
AI summary
An object detection device including at least one image capture element can capture image data in a field of view and detect types of objects located in that region. Information such as a direction of travel or speed of the object may be used to determine a relative position of the object of interest, for example relative to the object detection device or a target object. The data from multiple devices for a region can be aggregated such that objects can be tracked as they move though the region. Information about the relative position of the object of interest can be used to trigger alerts to users in the area.