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Rapid robust target tracking method based on sparse compact correlation filter

A correlation filter, target tracking technology, applied in the field of computer vision, can solve problems such as accuracy dependence

Active Publication Date: 2020-10-30
XIAMEN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Most of this offline training method can achieve real-time, but its accuracy depends on the network and data used for training

Method used

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  • Rapid robust target tracking method based on sparse compact correlation filter
  • Rapid robust target tracking method based on sparse compact correlation filter
  • Rapid robust target tracking method based on sparse compact correlation filter

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Embodiment Construction

[0058] The present invention belongs to the target tracking method of the correlation filtering class, and the following embodiments will further illustrate the present invention.

[0059] Embodiments of the present invention include the following steps:

[0060] A. At frame t, for a given target, a basic sample is constructed from the target and its context. The training sample is composed of all circular translation samples of this basic sample. The labels of these circular translation samples are determined by the Gaussian function, and DCF training is more The loss function of the channel correlation filter is defined as follows:

[0061]

[0062] in, is the circular convolution operation symbol, X t ∈R M×N×D and W t ∈R M×N×D is the basic sample and filter of frame t, Y∈R M×N is the label determined by the Gaussian function, M, N and D represent the width, height and channel number respectively, and ξ is the parameter of the regular term; the goal of filter learn...

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Abstract

The invention discloses a rapid robust target tracking method based on a sparse compact correlation filter, and relates to the computer vision technology. The method includes: constructing a basic sample by the target and the context thereof, forming a training sample by all cyclic translation samples of the basic sample, and training a loss function of a multi-channel correlation filter by the DCF; integrating exclusive sparse regular terms and group sparse regular terms in multi-task learning to construct intra-group and inter-group sparse regular terms, introducing time consistency constraints in target tracking to alleviate the problem of degradation of DCF with time, introducing the intra-group and inter-group sparse regular terms and the time regular terms to define a regression lossfunction, and learning a sparse correlation filter; integrally removing the redundant filters through channel pruning, sorting the D-channel filters according to the importance degree, and selectingthe channel filter sorted in the front for tracking; and constructing a Lagrange function, and optimizing regression loss by adopting an ADMM algorithm. The discriminability and interpretability of the filter are effectively improved, the precision is high, and the speed is high.

Description

technical field [0001] The invention relates to computer vision technology, in particular to a fast and robust tracking method based on sparse compact correlation filters. Background technique [0002] Human beings have a high visual perception ability for external videos, and the brain can quickly and accurately locate moving targets in the video. The computer must imitate the visual perception ability of the human brain, and must reach the human level in speed and precision. Visual tracking is a basic problem in computer vision and the basic content of visual perception. Its speed and accuracy determine the real-time and accuracy of visual perception. Object tracking is one of the important research directions in the field of computer vision, and it plays an important role in intelligent video surveillance, human-computer interaction, robot navigation, virtual reality, medical diagnosis, public safety and other fields. The task first selects the object of interest in the...

Claims

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Application Information

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IPC IPC(8): G06T7/277
CPCG06T7/277G06T2207/20081
Inventor 王菡子梁艳杰熊逻
Owner XIAMEN UNIV
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