Computer vision algorithms have become increasingly advanced over the past decades, enabling the development of sophisticated technologies to monitor specific environments, detect objects of interest in video footage and uncover suspicious activities in CCTV recordings. Some of these algorithms are specifically designed to detect and isolate moving objects or people of interest in a video, a task known as moving target segmentation.
While some conventional algorithms for moving target segmentation attained promising results, most of them perform poorly in real-time (i.e., when analyzing videos that are not pre-recorded but are being captured in the present moment). Some research teams have thus been trying to tackle this problem using alternative types of algorithms, such as so-called quantum algorithms.
Researchers at Nanjing University of Information Science and Technology and Southeast University in China recently developed a new quantum algorithm for the segmentation of moving targets in grayscale videos. This algorithm, published in Advanced Quantum Technologies, was found to outperform classical approaches in tasks that involve the analysis of video footage in real-time.
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