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Sensor-Enabled Intelligent Environments: A Practical Course on Barcode-Based Object Recognition

This advanced practical course explores the integration of sensor technology in intelligent environments, highlighting barcode-based object recognition. Presented by Nacer Khalil and supervised by Dejan Pangerčić, the course covers major components: autofocus mechanisms, barcode decoding with Zbar, and information retrieval from the Barcoo database. Techniques for barcode localization and their practical applications are demonstrated. The project’s future work includes ontology creation and integration with robotic platforms for enhanced object perception and interaction.

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Sensor-Enabled Intelligent Environments: A Practical Course on Barcode-Based Object Recognition

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  1. Advanced Practical Course: Sensor-enabled Intelligent EnvironmentsBarcode-based Object Recognition Final Presentation Presented by: Nacer KHALIL Supervised by: Dejan PANGERCIC

  2. Table of content I- Overall project goal II- Autofocus III- Bacode decoding IV- information retrieval V- Barcode localization VI- Conclusion

  3. II-AutofocusHow autofocus works • Active vs passive autofocus Courtesy of howstuffworks.com

  4. II-Autofocus(continued)

  5. II- AutofocusImplementation in the project • Used camera: Logitech QC PRO 9000 • Driver used: ROS::uvc_camera • Problem: Autofocus is not supported by the driver • Solution: • Autofocus was added to uvc_camera driver • Autofocus algorithm was taken from GUVCVIEW software and integrated within uvc_camera driver

  6. II- Autofocus result

  7. III-Barcode decodingHow Zbar works Courtesy of Jeff Brown

  8. IV-Information retrieval • Barcoo is a product information store that has a database composed of 7 million commercial objects. • Access to this database was granted to us. • Communication to the database is done through HTTP protocol. • Request: an http link containing the barcode • Response: XML file containing all information about the object http://www.barcoo.com

  9. IV- Information retrieval Barcoo request response example • Request: • http://www.barcoo.com/api/get_product_complete? Pi=73705207908 &pins=ean&amp ;format=xml&source=ias-tum • Response: We are parsing for: - Image - product name - category - producer

  10. V- Barcode localization Techniques used • Techniques used to find the barcode region of interest • Blob-based barcode localization • Parallel line-based localization • Adjacent line-based localization

  11. V- Barcode localization Blob-based localization(working example)

  12. V- Barcode localization Blob-based localization (not working example)

  13. V- Barcode localization Adjacent line-based localization

  14. V-Barcode localization How adjacent line-based localization works

  15. V-Barcode localization Adjacent line-based approach explanation - Takepicture • Convert to grayscale • Parameters: interval size, min/max # of transitions, max Jeffrie’s value, min # of rows per ROI Image matrix Transitions matrix Eliminated intervals

  16. V-Barcode localization Adjacent line-based approach explanation (continued) Jeffrie ’s distance matrix Eliminated intervals matrix Final matrix

  17. IV- Barcode localization Adjacent line-based localization - results

  18. Open Source Code Packages list: • zbar_barcode_reader_node • zbar_qt_ros • uvc_camera • barcode_detection Repositories: • http://code.cs.tum.edu/indefero/index.php//p/seie2011fall/source/tree/HEAD/khalil • http://code.cs.tum.edu/indefero/index.php//p/ias-perception/source/tree/master/

  19. Conclusion • Project is composed of three parts: • Barcode localization • Implementation of autofocus • Information retrieval of objects • Future work: • Creation of the barcoo ontology and storage on KnowRob • Integration and testing on PR2 • Integration with object modeling center

  20. Demonstrations of the project in the kitchen lab after the presentations end

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