Target tracking method fusing convolutional network features and discriminant correlation filter

A correlation filter and target tracking technology, applied in the field of target tracking, can solve problems such as poor performance of convolution features and achieve advanced performance

Active Publication Date: 2018-08-31
SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Since the derivation work is carried out in the Fourier frequency domain, the present invention not only retains the characteristics of high CF efficiency, but also uses the convolution feature to improve the feature representa

Method used

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  • Target tracking method fusing convolutional network features and discriminant correlation filter
  • Target tracking method fusing convolutional network features and discriminant correlation filter
  • Target tracking method fusing convolutional network features and discriminant correlation filter

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Experimental program
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Embodiment

[0029] Basic thought of the present invention is:

[0030] Establish an end-to-end lightweight network architecture, construct the relevant filter tracking component as a special layer that can be differentiated in the convolutional neural network to track the target, and derive the backpropagation by defining the network output as a probability map of the target position . During the tracking process, the target block and multiple background blocks are tracked at the same time. By perceiving the structural relationship between the target and the surrounding background blocks, a model is established for the highly recognizable part of the target and its surrounding environment. When large-area occlusion occurs and the target shape is extremely deformed In the case of difficult tracking, such as drastic changes in illumination, etc., it automatically uses the background part with high tracking reliability combined with the motion model to infer the position of the target.

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Abstract

The invention discloses a target tracking method fusing convolutional network features and a discriminant correlation filter. An end-to-end lightweight network system structure is established; and theconvolutional features are trained by learning rich stream information in continuous frames, thereby improving feature representation and tracking precision. A correlation filter tracking component is constructed as a special layer in a network to track a single image block; in the tracking process, a target block and multiple background blocks are tracked at the same time; by perceiving a structural relationship between a target and surrounding background blocks, a model is built for a part with a high discrimination degree for the target and a surrounding environment; a target tracking effect is measured through a relationship between a peak sidelobe ratio and a peak value of a confidence map; and under the condition of high tracking difficulty such as large-area shielding, target shapeextreme deformation, illumination drastic change, locating is performed by automatically utilizing the discriminated background part.

Description

technical field [0001] The invention relates to a target tracking method for fusing convolution network features and discriminant correlation filters. Background technique [0002] Target tracking is a basic problem in computer vision. A common process for this problem is to input a continuous video image, initialize the object of interest with a bounding box in the first frame, and estimate the target in subsequent frames. The location of the object. Visual tracking is an important technology in computer vision, which has a wide range of applications in security protection, intelligent monitoring, human-computer interaction, and automatic control systems. [0003] In recent years, many researchers have done a lot of research on visual object tracking based on discriminant correlation filter (DCF), and made great progress. With the development of methods, existing algorithms can solve the problem of motion tracking in simple motion environment well. However, previous work...

Claims

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Application Information

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IPC IPC(8): G06T7/246G06T7/277G06N3/04
CPCG06T7/246G06T7/277G06T2207/10016G06N3/045
Inventor 刘宁刘畅吴贺丰
Owner SUN YAT SEN UNIV
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