Abstract
Computer vision is a very important research direction in the cognitive computing field. Robots encounter various target-tracking problems with computer vision systems. Robust scale estimation is an important issue in tracking algorithms. Most of the available methods have difficulty addressing even reasonable changes of scale in complex videos. In this paper, we propose a visual tracking method based on robust scale estimation, which uses a discriminant correlation filter based on a time-dependent scale-space filter and an adaptive cross-correlation filter. The tracker uses separate essential filters for sample migration and scale estimation. Furthermore, the built-in scale estimation method can be introduced into other tracking algorithms. We validate the proposed method on the UAV123 dataset. The results of comparison experiments with the traditional correlation filter tracking method demonstrate that the proposed method improves the success rate and tracking accuracy while controlling the computational complexity; its success rate measured by the area under the curve is 0.638, while at a location error precision of 20%, it is 0.649.
Highlights
Computer vision is the basis of cognitive imaging and processing for robots
The improvements of this article mainly include the following: (1) We propose a spatial filtering prediction technology based on time domain correlation
When the circular matrix method is used, a sample can be used as a “base sample” to “copy” thousands of similar samples through circular displacement, but these samples are related to the base samples only after they are transferred to the Fourier domain, that is, these samples will not cause a calculation increase when they are calculated in the Fourier domain
Summary
Computer vision is the basis of cognitive imaging and processing for robots. Robots interact with the external environment in real time through computer vision systems. Wireless Communications and Mobile Computing the minimum output sum of squared error (MOSSE) filter method, whose discrimination model is based on the least square error, thereby creating a new tracking approach The principle of this model is simple, it operates rapidly, and it is better at distinguishing the target from the background. CSK optimized the training objective and derived the closed-form solution of the correlation filter It greatly simplified the matrix multiplication operation in the Fourier domain by using the characteristics of the circulant matrix while retaining the speed advantage of MOSSE and achieving an improved tracking effect. The improvements of this article mainly include the following: (1) We propose a spatial filtering prediction technology based on time domain correlation Using this technology substantially reduces the calculations needed for spatial filtering. A series of experiments compared with some existing traditional typical tracking algorithms are carried out to demonstrate the efficiency of the proposed method
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