Mobile Barcode Detection With Motion-Guided Selective Decoding
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Solution Overview
Problem
Mobile devices face challenges in accurately decoding barcodes in scenes with multiple patterns, leading to incorrect information retrieval, unnecessary battery drain, and increased processing power due to unintentional barcode scanning.
Innovation Solution
Implementing smart barcode detection techniques that track motion and use image scoring to identify and decode only the intended barcode, reducing duplicate scanning and processing power by tracking previously decoded barcodes and focusing on the intended pattern.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the camera captures and decodes all optical patterns in a scene, then complete barcode detection is achieved, but processing power and battery drain increase due to unnecessary decoding operations
Solution Approach 1:
The system performs preliminary actions by capturing multiple images before decoding, using image similarity scoring to pre-identify the intended barcode. This allows the system to select only the relevant barcode for decoding, avoiding unnecessary processing of other patterns in the scene and thereby reducing battery drain while maintaining detection accuracy.
Solution Approach 2:
The system extracts only the intended barcode from the set of detected optical patterns by using image similarity scoring and motion tracking. This extraction process filters out unnecessary barcodes and patterns, allowing the system to decode only the relevant information and reduce processing power consumption accordingly.
2Measurement precision
If the camera captures and decodes all optical patterns in a scene, then complete barcode detection is achieved, but processing power increases due to unnecessary decoding operations
Solution Approach 1:
The system performs preliminary actions by capturing multiple images before decoding, using image similarity scoring to pre-identify the intended barcode. This allows the system to select only the relevant barcode for decoding, avoiding unnecessary processing of other patterns in the scene and thereby reducing processing power consumption while maintaining detection accuracy.
Solution Approach 2:
The system extracts only the intended barcode from the set of detected optical patterns by using image similarity scoring and motion tracking. This extraction process filters out unnecessary barcodes and patterns, allowing the system to decode only the relevant information and reduce processing power consumption accordingly.
3Productivity
If the system decodes barcodes continuously without tracking motion, then all barcodes in view are processed, but duplicate scanning occurs and efficiency decreases
Solution Approach 1:
The system performs preliminary actions by capturing multiple images before decoding, using image similarity scoring to pre-identify the intended barcode. This allows the system to select only the relevant barcode for decoding, avoiding unnecessary processing of other patterns in the scene and thereby reducing processing power consumption while maintaining detection accuracy.
Solution Approach 2:
The system uses feedback from motion tracking and image similarity scoring to dynamically adjust which barcodes are decoded. By continuously monitoring motion and comparing image similarity, the system can identify when a barcode has been previously decoded and skip redundant processing, thereby eliminating duplicate scanning and improving overall efficiency.
Data Source
AI summary
Smart barcode detection can include capturing, using a camera, a plurality of images of a real scene comprising a plurality of optical patterns. A score may be calculated for each of the plurality of images for comparing scores for each of the plurality of images to a threshold value. Based on the first image having a score that meets or exceeds the threshold value, detecting the first optical pattern and the second optical pattern in the first image and calculating an optical-pattern score for the first optical pattern in the first image. The first optical pattern may be decoded after detecting the plurality of optical patterns in the first image based on the optical-pattern score and without decoding the second optical pattern.