Rip current images are useful for assisting in climate studies but time consuming to manually annotate by hand over thousands of images. Object detection is a possible solution for automatic annotation because of its success and popularity in identifying regions of interest in images, such as human faces. Similarly to faces, rip currents have distinct features that set them apart from other areas of an image, such as more generic patterns of the surf zone. There are many distinct methods of object detection applied in face detection research. In this thesis, the best fit for a rip current object detector is found by comparing these methods. In addition, the methods are improved with Haar features exclusively created for rip current images. The compared methods include max distance from the average, support vector machines, convolutional neural networks, the Viola-Jones object detector, and a meta-learner. The presented results are compared for accuracy, false positive rate, and detection rate. Viola-Jones has the top base-line performance by achieving a detection rate of 0.88 and identifying only 15 false positives in the test image set of 53 rip currents. The described meta-learner integrates the presented Haar features, which are developed in accordance with the original Viola-Jones algorithm. Ada-Boost, a feature ranking algorithm, shows that the newly presented Haar features extract more meaningful data from rip current images than some of the current features. The meta-classifier improves upon the stand-alone Viola-Jones when applying these features by reducing its false positives by 47% while retaining a similar computational cost and detection rate.
| Identifer | oai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:td-3579 |
| Date | 18 May 2018 |
| Creators | Maryan, Corey C |
| Publisher | ScholarWorks@UNO |
| Source Sets | University of New Orleans |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of New Orleans Theses and Dissertations |
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