A comparison of object detection algorithms using unmanipulated testing images: Comparing SIFT, KAZE, AKAZE and ORB
Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
While the thought of having computers recognize objects in images have been around for a long time it is only in the last 20 years that this has become a reality. One of the first successful recognition algorithms was called SIFT and to this day it is one of the most used. However in recent years new algorithms have been published claiming to outperform SIFT. It is the goal of this report to investigate if SIFT still is the top performer 17 years after its publication or if the newest generation of algorithms are superior.
By creating a new data-set of over 170 test images with categories such as scale,rotation, illumination and general detection a thorough test has been run comparing four algorithms, SIFT, KAZE, AKAZE and ORB. The result of this study contradicts the claims from the creators of KAZE and show that SIFT has higher score on all tests. It also showed that AKAZE is at least as accurate as KAZE while being significantly faster. Another result was that while SIFT, KAZE and AKAZE were relatively evenly matched when comparing single invariances that changed when performing tests that contained multiple variables. When testing detection in cluttered environments SIFT proved vastly superior to the other algorithms. This led to the conclusion that if the goal is the best possible detection in every-day situations SIFT is still the best algorithm.
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IdentifiersURN: urn:nbn:se:kth:diva-186480OAI: oai:DiVA.org:kth-186480DiVA: diva2:927387