Augmented Reality Item Tracking via Video Pattern Detection
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Solution Overview
Problem
Current systems for item relocation and tracking, such as traditional postal tracking systems, face challenges in providing accurate tracking information due to scan failures at distribution centers, leading to poor user experience and inefficiencies in logistics operations.
Innovation Solution
A system that utilizes augmented reality cues and location-based confirmation, integrating machine learning models and neural networks to monitor video streams from client devices, detect patterns, and provide visual directional cues for item relocation, while also predicting item locations and statuses using container shipping information and scan event data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional postal tracking systems are used, then tracking information can be obtained, but scan failures at distribution centers lead to inaccurate tracking information
Solution Approach 1:
The system continuously monitors the live video stream for patterns and compares detected item locations with expected destination locations, providing real-time feedback to confirm or correct relocation status. This feedback loop ensures accurate tracking information even when traditional scan systems fail.
Solution Approach 2:
The patent replaces the mechanical scan-based tracking system with an augmented reality vision system that uses computer vision and pattern recognition to detect item locations and verify relocation, eliminating dependency on physical scan operations at distribution centers.
2Measurement precision
If augmented reality cues and location-based confirmation are implemented, then item relocation accuracy is enhanced, but system complexity increases
Solution Approach 1:
The client device performs multiple functions including live video streaming, pattern detection, location determination, augmented reality presentation, and relocation confirmation, consolidating complex functionality into a single multi-functional system that reduces overall architectural complexity.
Solution Approach 2:
The system uses the client device's own camera and sensors to capture video streams and determine locations, making the device self-sufficient for tracking and verification without requiring separate specialized equipment at distribution centers.
3Speed
If continuous monitoring of live video streams is performed, then real-time item tracking is achieved, but computational resources and energy consumption increase
Solution Approach 1:
The system continuously monitors the video stream for patterns at regular intervals rather than processing every frame, achieving real-time tracking capability while reducing computational load and energy consumption through periodic pattern detection events.
Solution Approach 2:
The system pre-processes and analyzes video frames to identify patterns of interest before full processing, enabling rapid detection of item relocation events while minimizing continuous computational resource requirements for routine monitoring.
Data Source
AI summary
In certain embodiments, item relocation may be facilitated via augmented reality cues and location-based confirmation. In some embodiments, in response to a detection of a first pattern in a live video stream obtained at a client device, a first location associated with the client device may be obtained, and an augmented reality presentation of a visual directional cue may be presented on a user interface of the client device such that the visual directional cue is overlaid on the live video stream. The visual directional cue may include visual directions from the first location to a destination location. In response to an indication that the item has been relocated to the destination location, a determination may be made as to whether the client device is within a threshold distance from the destination location. A confirmation may be generated in response to the client device being within the threshold distance.