Adaptive tracking of correlated filtered video based on artificially combined depth features

Abstract

The invention discloses a correlation filtering video adaptive tracking method combined artificially with depth features, which comprises the following steps: pre-training a depth neural network; Depth convolution feature extraction; Manual feature extraction; Multi-feature fusion computation; Scale Adaptive Target Detection and Response Computation; Correlation filter model updating; Output current frame tracking results. As that depth convolution feature are adopted, The gradient direction histogram and color name feature are fused to model the appearance of the object, the background response is suppressed by the spatial context information of the object, the discriminant ability of the model is enhanced by the adaptive scale estimation method, and the computational complexity is reduced by the fast Fourier transform in the frequency domain. The invention improves the robustness of tracking under complex scenes, and can be widely applied to the fields of video surveillance, human-computer interaction, robot technology, road scene understanding and the like.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a correlation filtering video adaptive tracking method combining artificial and depth features. Background technique [0002] Target tracking is an important research content in the field of computer vision. Target tracking is mainly based on the position of the target in the first frame or the first few frames in the video to estimate the position trajectory of the target in the subsequent sequence. At present, the methods in the field of target tracking technology are mainly divided into two categories: [0003] (1) Generative method: This method mainly uses the generative model to describe the appearance characteristics of the target, and finds the region most similar to the target appearance in the subsequent sequence, that is, minimizes the reconstruction error by searching for candidate targets. More representative algorithms include sparse coding, Kalman filter, particle fi...

AI Technical Summary

Problems solved by technology

[0005] However, the traditional discriminative methods have an important defect, that is, in order to enhance the discriminative ability, a large number of training samples are often required, and at the same time, the computational burden is increased, making these discriminative methods poor in real-time tracking.

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