Inventory Video Tracking via Optical Flow and Symbology Decoding
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Solution Overview
Problem
Current inventory management systems are inefficient and prone to inaccuracies due to double scans, missed scans, and difficulties in handling mixed symbologies, leading to costly and time-consuming manual counting processes.
Innovation Solution
A method and system utilizing a portable computing device with a camera subsystem that captures video streams of inventory items, employing tracking points, optical flow, and feature learning techniques to identify and quantify items, while filtering out duplicates and decoding symbology, thereby improving inventory management efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If handheld scanners are used for inventory counting, then counting speed is improved compared to manual methods, but accuracy deteriorates due to double scans, missed scans, and inability to handle mixed symbologies
Solution Approach 1:
The patent replaces the mechanical handheld scanner system with an optical video-based system. The camera captures video frames that are processed through optical flow analysis to automatically track and count inventory items, eliminating the need for manual scanning operations and reducing human error in the counting process
Solution Approach 2:
The system creates a visual copy of the inventory scene through video capture and uses image processing to generate a digital representation of inventory items. This allows the system to analyze and count items based on their visual characteristics and symbology patterns without physical contact or manual intervention
2Measurement precision
If employees manually count and verify inventory, then accuracy can be maintained through careful verification, but time consumption and labor costs increase significantly
Solution Approach 1:
The system performs self-service inventory counting by automatically capturing video, processing frames through optical flow analysis, identifying items based on visual features and symbology, and generating counts without requiring employee intervention for each counting operation, thereby maintaining accuracy while dramatically reducing time and labor requirements
3Measurement precision
If scanners are configured to recognize specific symbology types, then reading accuracy for those types is improved, but versatility deteriorates when dealing with mixed symbologies
Solution Approach 1:
The system implements a universal symbology recognition approach by capturing the entire visual scene and analyzing multiple item types simultaneously. The optical flow and image processing algorithms can identify and read various symbology types (barcodes, QR codes, data matrices) within the same video frame, allowing the system to handle mixed symbologies with a single configuration
4Speed
If video streams are used to capture inventory, then real-time monitoring capability is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The system segments the video processing task into discrete frame-by-frame analysis. Each frame is processed independently through optical flow analysis to identify changes from the previous frame, allowing real-time processing by breaking down the continuous video stream into manageable units that can be handled sequentially with reduced computational complexity
Data Source
AI summary
A solution for inventory identification and quantification using a portable computing device (“PCD”) comprising a camera subsystem is described. An exemplary embodiment of the solution comprises a method that begins with capturing a video stream of a physical inventory comprised of a plurality of individual inventory items. Using a set of tracking points appearing in sequential frames, and optical flow calculations, coordinates for global centers of the frames may be calculated. From there, coordinates for identified inventory items may be determined relative to the global centers of the frames within which they are captured. Comparing the calculated coordinates for inventory items identified in each frame, as well as fingerprint data, embodiments of the method may identify and filter duplicate image captures of the same inventory item within some statistical certainty. Symbology data, such as QR codes, are decoded and quantified as part of the inventory count.