A quantum moving target segmentation algorithm based on mean background modeling

Classical algorithms for moving target segmentation have made significant progress, but the real-time problem has become a significant obstacle for them as the data volume grows. Quantum computing has been proven to be beneficial for image segmentation, but is still scarce for video. In this paper, a quantum moving target segmentation algorithm based on mean background modeling is proposed, which can utilize the quantum mechanism to do segmentation operations on all pixels in a video at the same time. In addition, a quantum divider with lower quantum cost is designed calculate pixel mean, and then, a number of quantum modules are designed according to the algorithmic steps to build the complete quantum algorithmic circuit. For a video containing \(2^m\) $2^m$ frames (every frame is a \(2^n \times 2^n\) $2^n\times 2^n$ image with q grayscale levels), the proposed algorithm is superior compared to both existing quantum and classical algorithms. Finally, the experiment on IBM Q shows the feasibility of the algorithm in the NISQ era.