Automated parking space detection
| dc.contributor.author | Nyambal, Julien Cedric | |
| dc.date.accessioned | 2018-09-10T10:14:56Z | |
| dc.date.available | 2018-09-10T10:14:56Z | |
| dc.date.issued | 2018 | |
| dc.description | A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfillment of the requirements for the degree of Master of Science, Johannesburg, 2018. | en_ZA |
| dc.description.abstract | Parking space management is a problem that most big cities encounter. Without parking space management strategies, the traffic can become anarchic. Compared to physical sensors around the parking lot, a camera monitoring it can send images to be processed for vacancy detection. This dissertation implements a system to automatically detect and classify spaces (vacant or occupied) in images of a parking lot. Detection is done using the Region based Convolutional Neural Networks (RCNN). It reduces the amount of time that would otherwise be spent manually mapping out a parking lot. After the spaces are detected, they are classified as either vacant or occupied. It is accomplished using the Histograms of Oriented Gradients (HOG) with the Linear and Radial Basis Function (RBF) Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and a Hybrid approach. The classifiers are trained, tested and validated using data collected for this research. We compared the results of the Hybrid classifier against CNN and SVMs. The Hybrid classifier performed better than all the other ones with an accuracy of 89.36% and a precision of 82.54%, which is the best score obtained from all the other classifiers used. Novel contributions of this work include the new labeled database, the use of the RCNN for bay detection, and the classification of bays using the hybrid CNN and SVM. | en_ZA |
| dc.description.librarian | LG2018 | en_ZA |
| dc.format.extent | Online resource (74 leaves) | |
| dc.identifier.citation | Nyambal, Julien Cedric, (2018) Automated parking space detection, University of the Witwatersrand, Johannesburg, https://hdl.handle.net/10539/25627. | |
| dc.identifier.uri | https://hdl.handle.net/10539/25627 | |
| dc.language.iso | en | en_ZA |
| dc.subject.lcsh | Automation | |
| dc.subject.lcsh | Neural networks (Computer science) | |
| dc.title | Automated parking space detection | en_ZA |
| dc.type | Thesis | en_ZA |
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