Stationary Tracker for Privacy-Preserving Visual Asset Tracking
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Solution Overview
Problem
Existing image tracking systems face challenges in providing fine-granular location tracking of assets, especially in urban areas where GPS signals are obstructed, and struggle to protect privacy by masking non-target individuals in visual captures.
Innovation Solution
The use of stationary trackers equipped with cameras, wireless transceivers, geographic location detectors, and blur generators to generate masked images by blurring or obscuring non-target features in real-time, distributing processing requirements across multiple trackers to offload workload from cloud services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS tracking is used for asset location tracking, then location tracking capability is improved, but it fails in urban areas where GPS signals are obstructed
Solution Approach 1:
The system divides location tracking into two complementary segments: GPS-based tracking for open areas and visual capture-based tracking for GPS-obstructed urban areas. This segmentation allows the system to maintain reliable location tracking across diverse environments by switching between or combining different tracking methods depending on the operational context.
Solution Approach 2:
The patent introduces stationary trackers with cameras as an intermediary system to supplement GPS tracking. These trackers capture visual images of assets and their locations, creating an alternative tracking mechanism that operates independently of GPS signals, thereby resolving the limitation of GPS in obstructed areas.
2Measurement precision
If visual captures are transmitted without masking, then location tracking precision is improved, but privacy of non-target individuals is compromised
Solution Approach 1:
The system applies different quality treatments to different regions within the same visual capture. Target assets maintain clear, unmasked images for precise tracking, while non-target individuals and background elements are selectively blurred or masked. This local differentiation preserves tracking precision for assets while protecting the privacy of others in the same image.
Solution Approach 2:
The system extracts and isolates the target asset from the rest of the visual scene by applying masking techniques to non-target elements. This extraction allows the target's location to be precisely tracked while removing potentially harmful privacy exposures of other individuals in the captured image.
3Device complexity
If all processing is performed on cloud services, then system complexity is reduced, but processing speed and computational efficiency deteriorate
Solution Approach 1:
The processing workload is segmented and distributed between edge devices (stationary trackers with local processing capability) and cloud services. The stationary trackers perform initial image processing, masking, and asset identification locally, then transmit only essential processed data to the cloud. This segmentation reduces cloud processing burden and improves overall system speed.
Solution Approach 2:
The stationary trackers perform preliminary processing actions locally before data transmission to the cloud. By conducting initial image analysis, asset detection, and privacy masking at the edge, the system reduces the volume and complexity of data requiring cloud processing, thereby improving processing speed while maintaining architectural simplicity.
Data Source
AI summary
An example stationary tracker includes memory to store fixed geographic location information indicative of a fixed geographic location of the stationary tracker, and to store a reference feature image; and at least one processor to: determine a feature in an image is a non-displayable feature by comparing the feature to the reference feature image; and generate a masked image, the masked image to mask the non-displayable feature based on the non-displayable feature not allowed to be displayed when captured from the fixed geographic location of the stationary tracker, and the masked image to display a displayable feature in the image; and a wireless interface to detect a wireless tag located on a tag bearer, the at least one processor to determine the tag bearer is the displayable feature in the image based on the wireless tag.