Inventory Item Counting Using Multi-Angle HOG and Depth Mapping
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Solution Overview
Problem
Current inventory tracking systems in materials handling facilities face challenges in accurately counting and monitoring inventory items due to variations in item orientation, position, and stacking configurations, leading to inefficiencies in item detection and counting processes.
Innovation Solution
The implementation of a system utilizing multiple histogram of oriented gradients (HOG) models and depth information from cameras to detect and count inventory items, with cameras positioned to capture images from various angles and orientations, and combining this data with depth information to confirm item features and prevent duplicate counting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-camera or barcode tracking systems are used, then the system complexity is low, but the measurement precision of inventory counting deteriorates due to variations in item orientation, position, and stacking configurations
Solution Approach 1:
The patent segments the inventory monitoring task into multiple specialized camera views (overhead, front, side, rear) positioned at different locations. Each camera captures specific aspects of item orientation and stacking, allowing the system to process complex spatial information through divided sensory inputs rather than relying on a single complex sensor system.
Solution Approach 2:
The patent transitions from 2D barcode recognition or single-view imaging to 3D spatial monitoring by deploying cameras at multiple elevations and angles. This multi-dimensional approach captures items in various orientations and stacking configurations, enabling accurate counting regardless of item position or orientation through volumetric surveillance.
2Measurement precision
If multiple cameras positioned at various angles are deployed to capture item orientations, then the measurement precision of item detection improves, but the device complexity and data processing requirements worsen
Solution Approach 1:
The patent employs a standardized camera system design where identical or similar camera units are deployed at multiple locations (overhead, front, side, rear positions). Each camera performs the same basic function of capturing item imagery, but their combined multi-position deployment provides comprehensive spatial coverage, allowing the system to handle various item orientations and configurations through a universal sensor platform.
3Measurement precision
If depth information is combined with image data to confirm item features, then the measurement precision of inventory counting improves by preventing duplicate counting, but the use of energy and data processing requirements worsen
Solution Approach 1:
The patent introduces depth information as an intermediary data layer that mediates between the raw image data from multiple cameras and the final item counting results. This depth map serves as a computational mediator that helps distinguish actual item boundaries from visual artifacts or duplicate detections, enabling more accurate counting by providing spatial verification without requiring complex analysis of all image data.
Data Source
AI summary
Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.