Object tracking method based on densely connected convolutional network

Abstract

The present invention discloses a target tracking method based on a densely connected convolutional network, comprising the following steps: S1, determining the size and position of the target of interest; S2, extracting the convolution features of the input frame and making a judgment, if the input frame is the initial frame , then obtain the PCA projection matrix to reduce the dimensionality of the convolution features, use the obtained convolution features to train the target tracking model based on the densely connected network, and enter S7, otherwise use the trained PCA projection matrix to reduce the dimensionality of the input frame convolution features, Enter S3; S3, input the convolution feature into the tracking model to predict the position of the target of interest; S4, perform scale sampling at the predicted position of the target, and estimate the size of the target; S5, update the network weight of the target tracking model; S6, output the predicted position of the target and Scale; S7. Input the next frame until the prediction of all frames of the video is completed. The invention realizes the end-to-end learning of the tracking model, effectively reduces the training time, and improves the use efficiency.

Description

technical field [0001] The invention relates to a target tracking method, in particular to a target tracking method based on a densely connected convolutional network, and belongs to the technical field of target tracking. Background technique [0002] Object tracking is an important research field in computer vision, which is widely used in security monitoring, unmanned driving, human-computer interaction and so on. The main purpose of object tracking is to estimate the motion state of a given object of interest in a video. As a hot topic, object tracking has achieved many outstanding research results in recent years. However, since the changes in illumination, changes in target appearance, and background occlusion during use will pose great challenges to target tracking algorithms, the research on target tracking algorithms still needs to be in-depth. [0003] In recent years, the target tracking algorithm based on correlation filtering has attracted the attention of man...

AI Technical Summary

Problems solved by technology

Since the pre-training model for extracting convolutional features and correlation filtering are independent of each other, it does not reflect the advantages of neural network end-to-end learning

At the same time, the boundary effect brought by cyclic sampling also seriously limits the performance of the correlation filter tracking algorithm.

In addition, in the training process of the above algorithm, a large amount of data needs to be used and a high cost of time is required, which is not convenient for daily application

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