Template matching tracking method and system based on depth feature fusion
A technology of template matching and depth features, applied in the field of target tracking, can solve problems such as insufficient robustness, lack of real-time performance, and easy drift, etc., and achieve the effect of improving robustness and suppressing jitter and drift
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Publication Date
- 2021-01-29
Smart Images

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Abstract
Description
technical field
[0001] The invention relates to a template matching tracking method and system based on deep feature fusion, in particular to the technical field of target tracking. Background technique
[0002] With the development of computer technology, detection methods based on deep learning gradually occupy a dominant position in the fields of target detection, classification, and segmentation. Due to factors such as occlusion, illumination, and target non-rigidity in actual scenes, the accuracy and robustness of target tracking still have problems.
[0003] In the prior art, when using the depth feature stream to process images, the feature of filtering the still object to establish the feature of the moving object is adopted to propagate the feature of the key frame moving object to the current frame. This technology is prone to drift in the processing process, resulting in insufficient robustness. At the same time, obtaining the depth frame and feature information ...
Examples
Embodiment Construction
[0042] The present invention realizes the purpose of target tracking through a template matching tracking method based on deep feature fusion and a system for realizing the method. Below through embodiment, in conjunction with accompanying drawing, this scheme is described further in detail.
[0043] In this application, we propose a template matching tracking method based on deep feature fusion and a system for implementing the method, which includes a template matching tracking method based on deep feature fusion, as shown in the attached figure 1 Shown, be that the inventive method realizes flowchart, specifically divide into the following steps:
[0044] Step 1: Obtain video data, and input the first frame image of the video into the deep convolutional network; this step further preprocesses the acquired video data, specifically processing the size of the image to be input into the deep convolutional network into a deep convolutional network The acceptable size of the pro...