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Anomaly Detection for Product Inspection and Surveillance Applications
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Anomalidetektion för produktinspektions- och övervakningsapplikationer (Swedish)
Abstract [en]

Anomaly detection is a general theory of detecting unusual patterns or events in data. This master thesis investigates the subject of anomaly detection in two different applications. The first application is product inspection using a camera and the second application is surveillance using a 2D laser scanner.

The first part of the thesis presents a system for automatic visual defect inspection. The system is based on aligning the images of the product to a common template and doing pixel-wise comparisons. The system is trained using only images of products that are defined as normal, i.e. non-defective products. The visual properties of the inspected products are modelled using three different methods. The performance of the system and the different methods have been evaluated on four different datasets.

The second part of the thesis presents a surveillance system based on a single laser range scanner. The system is able to detect certain anomalous events based on the time, position and velocities of individual objects in the scene. The practical usefulness of the system is made plausible by a qualitative evaluation using unlabelled data.

Place, publisher, year, edition, pages
2015. , 70 p.
Keyword [en]
Anomaly detection, product inspection, surveillance, computer vision
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:liu:diva-121467ISRN: LiTH-ISY-EX--15/4889--SEOAI: diva2:855502
External cooperation
Subject / course
Computer Vision Laboratory
2015-09-18, Algoritmen, Linköping, 09:00 (Swedish)
Available from: 2015-09-22 Created: 2015-09-21 Last updated: 2015-09-22Bibliographically approved

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