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Robust target tracking method based on deep learning and multi-scale correlation filtering

A multi-scale correlation and deep learning technology, applied in image data processing, instruments, calculations, etc., can solve the time-consuming and large number of target tracking problems, and achieve the effect of avoiding the process of extracting a large number of samples

Active Publication Date: 2016-07-06
XIAN ANMENG INTELLIGENT TECH CO LTD
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Problems solved by technology

[0005] The present invention solves the following two problems in the prior art: 1) The prior art only utilizes the last layer result in CNN to represent the target, but the tracking task not only needs the semantic information of the target, but also needs the spatial structure information to be accurate 2) Online training of a classifier in the prior art requires a large number of positive and negative samples, which is very time-consuming for target tracking

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  • Robust target tracking method based on deep learning and multi-scale correlation filtering
  • Robust target tracking method based on deep learning and multi-scale correlation filtering
  • Robust target tracking method based on deep learning and multi-scale correlation filtering

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Embodiment Construction

[0039] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0040] Step 1: Read the first frame of image data and the position information [x, y, w, h] of the target block in the first frame of image, where x, y represent the horizontal and vertical coordinates of the target center, w, h represent the target width and height.

[0041] Step 2: Based on the target determination of the current frame image, extract the search area R centered on (x, y), use CNN to extract the convolution feature map, and upsample the feature map to the search area by bilateral interpolation method The size of R gets the convolutional feature map The size of R is M×N, M and N are width and height respectively, M=2w, N=2h, The size is M×N×D, D is the number of channels, l is the number of layers in the CNN, and its value is {37, 28, 19,} The present invention specifically uses VGGNet-19 as the CNN model.

[0042] Step 3: For the convolution...

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Abstract

The invention relates to a robust target tracking method based on deep learning and multi-scale correlation filtering. The tracking process is divided into a target location part and a scale selection part. In the target location part, the position of a target is located through a convolutional neural network and correlation filtering. In the scale selection part, a scale pyramid is used, and different scales are selected in a matching manner for targets through scale filtering. The multilayer characteristic of the convolutional neural network is taken as a representation model of targets, so the structural and semantic information of targets can be described robustly. Through use of the characteristics of correlation filtering, there is no need to train a classifier online, and the running speed of the algorithm is increased greatly. The idea of scale pyramid is adopted in scale, and correlation filtering matching is performed on targets of different scales to select the optimal scale. The method is of strong robustness to deformation, shading and scale change of targets.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a robust target tracking method based on deep learning and multi-scale correlation filtering. Background technique [0002] Target tracking algorithms can usually be divided into two categories: generative and discriminative. Generative tracking algorithms usually need to generate an appearance model for the tracked target, and find the candidate target with the highest similarity as the tracking result by matching the appearance model. The discriminative tracking algorithm regards tracking as a binary classification problem, and trains a classifier through positive and negative samples to distinguish the target from the background. [0003] In recent years, image processing and machine vision methods based on deep learning have received great attention, especially in the application of speech and image classification and recognition, but it has just started in target...

Claims

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Application Information

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IPC IPC(8): G06T7/20
CPCG06T2207/20016G06T2207/20081G06T2207/20084
Inventor 李映杭涛
Owner XIAN ANMENG INTELLIGENT TECH CO LTD
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