Scale adaptive target-tracking method based on depth characteristic kernel correlation filter

Abstract

The invention discloses a scale adaptive target-tracking method based on a depth characteristic kernel correlation filter. The method comprises the following steps: inputting an image into a pre-trained convolution neural network, and extracting depth convolution features; tracking a target, and estimating the position and scale of the target through the trained model; training the kernel correlation filter according to the currently detected target position and scale; and updating the kernel correlation filter through employing an adaptive high-confidence model updating method. According to the invention, the depth convolution features are extracted, and the adaptive scale estimation method and the adaptive high-confidence model updating strategy are improved, thereby improving the target tracking robustness under the conditions of complex scenes and appearance changes. The method can achieve the high-efficiency and accurate processing of the scale change of the target. In addition, the adaptive high-confidence model updating strategy is employed, so the model tracking drift is reduced as much as possible.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a scale-adaptive target tracking method based on a deep feature kernel correlation filter. Background technique [0002] In recent years, with the emergence of large-scale labeled data sets and the improvement of computer computing power, deep learning methods, especially convolutional neural networks, have been successfully applied to computer vision fields such as image classification, target detection, target recognition, and semantic segmentation. Thanks to the powerful object representation ability of convolutional neural network. Different from traditional image features, deep convolutional features are learned from a large number of thousands of categories of image data, so high-level convolutional features represent the semantic features of the target and are suitable for image classification problems. Due to the low resolution of high-level convolutional feature...

AI Technical Summary

Problems solved by technology

However, the kernel correlation filter algorithm uses the traditional gradient orientation histogram feature, and the tracking algorithm is prone to drift when the appearance representation of the target changes; in addition, the algorithm cannot estimate the scale change of the target, and uses simple linear interpolation to update the model, so When an error occurs in target tracking, the tracking algorithm drifts due to the update mechanism

Images