COMPARISON OF SUPPORT VECTOR CLASSIFICATION WITH KEY POINTS AND NEURAL NETWORKS FOR OBJECT DETECTION

V.-T. Luchka, V. Vdovychenko, Yuriy Furgala

Abstract


Object detection plays a crucial role in computer vision applications, ranging from autonomous driving to facial recognition. Over the years, researchers have developed various techniques to tackle the challenges of object detection. Among them, feature detection algorithms and neural networks have emerged as powerful approaches. This paper aims to provide a comparative analysis of these two methodologies, exploring their strengths and weaknesses.

Key words: Key points, support vector classification, neural networks, SIFT, ORB, BRISK, FREAK, YOLOv5


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References


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DOI: http://dx.doi.org/10.30970/eli.25.7

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