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Detecting Sitting People: Image classification on a small device to detect sitting people in real-time video
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The area of computer vision has been making big improvements in the latest decades, equally so has the area of electronics and small computers improved. These areas together have made it more available to build small, standalone systems for object detection in live video. This project's main objective is to examine whether a small device, e.g. Raspberry Pi 3, can manage an implementation of an object detection algorithm, called Viola-Jones, to count the occupancy of sitting people in a room with a camera. This study is done by creating an application with the library OpenCV, together with the language C+ +, and then test if the application can run on the small device. Whether or not the application will detect people depends on the models used, therefore three are tested: Haar Face, Haar Upper body and Haar Upper body MCS. The library's object detection function takes some parameters that works like settings for the detection algorithm. With that, the parameters needs to be tailored for each model and use case, for an optimal performance. A function was created to find the accuracy of different parameters by brute-force. The test showed that the Haar Face model was the most accurate. All the models, with their most optimal parameters, are then speed-tested with a FPS test on the raspberry pi. The result shows whether or not the raspberry pi can manage the application with the models. All models could be run and the Haar face model was fastest. As the system uses cameras, some ethical aspects are discussed about what people might think of top-corner cameras.

Place, publisher, year, edition, pages
2017. , p. 41
Keywords [en]
OpenCV, image-processing, AdaBoost, Viola-Jones, Raspberry Pi, Haar cascades, Haar features
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:miun:diva-31017Local ID: DT-V17-G3-024OAI: oai:DiVA.org:miun-31017DiVA, id: diva2:1115581
Subject / course
Computer Engineering DT1
Educational program
Computer Science TDATG 180 higher education credits
Supervisors
Examiners
Available from: 2017-06-27 Created: 2017-06-27 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
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