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Real-world CCTV footage often poses increased challenges in object tracking due to Pan-Tilt-Zoom operations, low camera quality and diverse working environments. Most relevant challenges are moving background, motion blur and severe scale changes. Convolutional neural networks, which offer state-of-the-art performance in object detection, are increasingly utilized to pursue a more efficient tracking...
The scale invariant feature transform, SIFT, is one of the most efficient image matching techniques based on local features. It has been applied to various scientific domains such as machine vision, robot navigation, object recognition, etc. In this work, a SIFT improvement is proposed that makes feature matching more robust in the presence of different types of image noise. Thus, Kendall's rank correlation...
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