Single target tracking method based on convolution neural network

A convolutional neural network and single-target technology, which is applied in the field of computer vision tracking, can solve problems such as illumination changes, occlusions, and deformations that are not robust, and achieve the effects of improving robustness, accuracy, and accuracy

Inactive Publication Date: 2017-05-24
BEIJING UNIV OF TECH
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Problems solved by technology

The features usually used in these two methods are some manually extracted features. These low-level manual fe

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  • Single target tracking method based on convolution neural network
  • Single target tracking method based on convolution neural network
  • Single target tracking method based on convolution neural network

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Embodiment Construction

[0029] The present invention will be further described below in combination with specific embodiments.

[0030] (1) Construct and train the network model

[0031] This method uses the labeled data set to pre-train a network model offline. The function of the network model is to extract and match the features of each candidate area input into the network, and calculate the score of each candidate area, so as to distinguish the input Which of the candidate regions are target objects and which are not. Then in the actual tracking test, first use the current tracking video information to fine-tune the network online, so that it can achieve the effect of being able to well adapt to tracking the current target.

[0032] Step 1: First, prepare the data set to be used for offline pre-training of the network model. The test data set of this method is the OTB50 data set, and the training data set is the VOT data set. OTB is a standard tracking benchmark data set, which contains 50 ful...

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Abstract

The invention discloses a single target tracking method based on a convolution neural network. The method comprises the steps that training data are used to preliminarily train a six-layer convolution neural network classification model; when tracking test is carried out, Ground-Truth information given by the first frame of a video is used to extract some sample data; through parameter fine-tuning of the network model, the network model can better adapt to the currently tracked video sequence; a Bounding Box regression model is trained for a currently tracked target to optimize a tracking result; and for a correct tracking result, the Bounding Box regression model is used to optimize the tracking result to acquire the accurate position of the target object. When tracking is carried out, network model parameters are timely and appropriately updated, so that the model can better adapt to the currently tracked video sequence. According to the invention, a pooling layer in the network structure is improved; a detection module is added, which makes the performance of a tracker more robust; and the tracking precision is improved.

Description

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Application Information

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Owner BEIJING UNIV OF TECH
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