New quantum algorithm for visual tracking

Shang Gao and Yu-Guang Yang

Physica A: Statistical Mechanics and its Applications, 2023, vol. 615, issue C

Abstract: Visual tracking, which trains a classifier to distinguish the target from the surrounding environment given an initial sample patch containing the target, plays an important role in computer vision. Yu et al. proposed a quantum algorithm for visual tracking (QVT) [Phys. Rev. A 94, 042311 (2016)] with time complexity OϱZϱZ+ϱX2polylogNϵ based on the framework proposed by Henriques et al. [IEEE Trans. Pattern Anal. Mach. Intell. 7, 583 (2015)], where ϱXZ is the condition number of the data matrix XZ, N is the dimension of the original sample patch, and ϵ is the desired accuracy of the output state. To get a further speedup, we propose a new QVT with time complexity OϱZ1+ϱXpolylogNϵ based on the algorithm of Henriques et al. Our algorithm achieves a quadratic speedup on the condition number ϱX(Z) compared to the algorithm of Yu et al. Also, it shows exponential speedups on N over the classical counterpart when ϱX(Z) and ϵ are OpolylogN. Finally, we extend it to the nonlinear two-dimensional multi-channel case.

Keywords: Quantum algorithm; Visual tracking; Nonlinear two-dimensional multi-channel; Time complexity; Quadratic speedup (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:615:y:2023:i:c:s0378437123001425

DOI: 10.1016/j.physa.2023.128587

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