Reliability-based multi-layer depth feature target tracking method
A target tracking and deep feature technology, applied in the field of network communication, can solve problems such as single feature, inability of tracker to distinguish, insufficient expression of tracking target, etc.
Pending Publication Date: 2021-07-06
JIANGSU UNIV OF TECH +1
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Problems solved by technology
[0004] Existing technical defects: the traditional target tracking method based on correlation filtering uses a single feature to represent the target, resulting in insufficient expressiveness of the feature for the current tracking target, especially when there is similar target interference in the background, the tracker cannot distinguish , even tracking fails with
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[0045] The following is a clear and complete description of the technical solutions in the implementation of the present invention in conjunction with the accompanying drawings, and the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.
[0046] Such as Figure 1 to Figure 5 As shown, a kind of reliability-based multi-layer depth feature target tracking method provided by the present invention comprises the following steps:
[0047] Step S1, input picture I according to the first frame of the video sequence 1 and the target box, and the subsequent input picture I t , t∈[2, N], N is the total number of frames in the video sequence, (x, y) is the center point coordinates, c=(c w , c h ) is the target scale;
[0048] St...
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The invention discloses a reliability-based multi-layer depth feature target tracking method. The characterization capability of different-layer features is measured by calculating the reliability of a channel by utilizing different discrimination capabilities of different-layer features in a tracking scene, so that the positioning information of the different-layer features is fused, and more accurate target position information is obtained. In the tracking process, a scale pool technology is adopted to update the size of a target in real time, and features extracted from a previous frame are fused with original template features to obtain a tracking model with higher robustness. According to the method, the representation capability of the model is improved, and more accurate target positioning is realized; the generalization ability of the model is improved; the ability of the tracking model to cope with the target scale change in a complex scene is improved; the robustness of the tracking model for dealing with target characterization change and external interference in a tracking scene is improved; compared with an existing common comparison algorithm, the method shows better tracking precision and success rate.
Description
technical field [0001] The invention relates to a reliability-based multi-layer depth feature target tracking method, which belongs to the technical field of network communication. Background technique [0002] Object tracking is a research hotspot in the computer field, and has a wide range of applications in video surveillance, human-computer interaction, intelligent transportation and other fields. The goal of the visual tracking task is to detect continuously moving objects in the image sequence, obtain the motion information of the object, further extract the motion trajectory of the object, and analyze the motion of the object, so as to realize the understanding of the motion behavior of the object. Due to the diversity and complexity of tracking scenarios, the existing target tracking algorithms are still not accurate enough to identify and locate targets. How to further improve the performance of existing target tracking algorithms has very important research signifi...
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Patent Timeline
Login to View More IPC IPC(8): G06T7/246G06K9/62G06N3/04G06N3/08
CPCG06T7/246G06N3/08G06T2207/10016G06T2207/20081G06T2207/20084G06T2207/20056G06N3/045G06F18/253
Inventor 尹明锋周文娟游丽萍花旭陈昌凯金圣昕周林苇贝绍轶
Owner JIANGSU UNIV OF TECH