A target tracking method based on depth feature and average peak correlation energy

Abstract

The invention provides a target tracking method based on depth feature and average peak correlation energy. The method comprises the following steps: extracting color histogram feature of the target,depth feature and three-layer depth feature of upper, lower, left and right image blocks of the target, and calculating a color histogram discrimination model and a depth feature model; calculating the color histogram characteristic response and the depth characteristic response of the target of the current frame, and predicting the target position of the next frame; calculating an average peak correlation energy of the target response of the current frame; if the average peak correlation energy of the current frame target response is greater than the average peak correlation energy of all frames before the current frame, determining that the confidence level of the frame response is high, and updating the color histogram discriminant model and the depth feature model by using a layered model update scheme, otherwise, the color histogram discriminant model and the depth feature model being not updated; repeating the above steps until the video sequence ends. The invention effectively fuses the depth feature and the average peak correlation energy, and adopts a layered model updating scheme to further effectively improve the tracking performance.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a target tracking method based on depth features and average peak correlation energy. Background technique [0002] The main task of visual tracking is to estimate the trajectory of the target motion in the video, which is one of the basic problems of computer vision. In recent years, object tracking methods have made great progress, but due to the interference of background disturbance and fast motion in the tracking process, the research of object tracking is still difficult. Current object tracking methods mainly consist of two categories: generative methods and discriminative methods. The generation method first extracts the target features of the current frame, generates the appearance model of the target, and uses this model to match in a new frame of image, and the object that best matches this model in the image is the target. Common generative methods includ...

AI Technical Summary

Problems solved by technology

With the increasing development of deep learning, there has been a method of using deep features for target tracking. ChaoMa et al. used a convolutional neural network model to extract multi-layer convolutional features for target tracking. Compared with the above artificial features, the performance has improved, but Can't track well when there is background disturbance and fast movement

Images