Minimum obstacle distance weighted tracking method

A distance-weighted, obstacle technology, applied in the field of computer vision, can solve the problems of inability to accurately distinguish the target and the background, severe occlusion, rapid deformation and illumination changes, etc., to solve occlusion and deformation, enhance stability and robustness, and reduce drift. Effect

Active Publication Date: 2018-12-04
ANHUI UNIVERSITY
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Problems solved by technology

However, it defines the static structure graph of the eight-neighborhood, and the edge weight is calculated based on the low-level feature distance. It performs poorly in dealing with severe occlusion, rapid deformation, and illumination changes in the tracking sequence.
[0004] The disadvantage of the existing technology is: th

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Embodiment Construction

[0046] The embodiments of the present invention are described in detail below. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following implementation example.

[0047] Such as figure 1 As shown, this embodiment includes the following steps:

[0048] (1) Input a certain frame of the video sequence, set a search window, and perform sampling in the search window to generate a group of candidate samples;

[0049] You can first input a certain frame of the video sequence, first set a search window in the current frame, and the search window is centered on the center corresponding to the target bounding box of the tracking result of the previous frame, with is the radius, where w is the width of the initial bounding box, h is the height of the initial bounding box, and sampli...

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Abstract

The invention discloses a minimum obstacle distance weighted tracking method. Sampling in a search window is carried out to generate a set of candidate samples; the boundary box of the current frame is divided to non-overlapped image blocks and is expanded to an expanded boundary box, and features of each image block are extracted; with a box image as a seed node, the minimum obstacle distance between other nodes and the seed node is calculated, and a distance conversion graph is then obtained; the minimum obstacle distance is added to the corresponding image block feature as a weight, a spatially-ordered weighted image block feature descriptor is obtained, and combination weights with image features are merged into a structured support vector machine to perform tracking. In combination ofimage color histogram features and directional gradient histogram features, the distance conversion graph based on a background seed node set is obtained through calculation, and according to the minimum obstacle distance between the image blocks and the background node, the problems of occlusion and deformation are solved, drift can be reduced and the tracking stability and the robustness are enhanced.

Description

technical field [0001] The invention relates to a computer vision technology, in particular to a minimum obstacle distance weighted tracking method. Background technique [0002] The widespread popularization of intelligent video surveillance systems has promoted the continuous deepening of computer vision theory research, and target tracking is an important issue in video analysis in the field of computer vision, and has a wide range of applications in surveillance, human-computer interaction and medical imaging. A typical target tracking scenario is that the position and size of the target to be tracked are given in the first frame of the video sequence, and the tracker is used to track the target in the next consecutive frames. Although the emergence of many tracking algorithms in recent years has promoted the development of object tracking, many factors faced in the tracking process, such as illumination changes, occlusion and rapid deformation, have not been well resolv...

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Application Information

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IPC IPC(8): G06T7/246G06T7/194
CPCG06T2207/10016G06T2207/30232G06T7/194G06T7/246
Inventor 涂铮铮郭林林李成龙江波汤进罗斌
Owner ANHUI UNIVERSITY
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