A Minimum Obstacle Distance Weighted Tracking Method

Abstract

The invention discloses a minimum obstacle distance weighted tracking method, which generates a group of candidate samples by sampling in a search window; divides the bounding box of the current frame into non-overlapping image blocks and expands them into extended bounding boxes, and extracts each image block features; use the border image as the seed node, calculate the minimum obstacle distance between other nodes and the seed node, and then obtain the distance conversion map; add the minimum obstacle distance as a weight to the corresponding image block feature, and obtain a spatially ordered weighted image block Feature descriptors, combined weights with image features are incorporated into a structured support vector machine to perform tracking. The present invention combines image color histogram features and direction gradient histogram features to calculate a distance conversion graph based on the background seed node set, and solves the problems of occlusion and deformation according to the minimum obstacle distance between the image image block and the background node, and can reduce drift at the same time , to enhance the stability and robustness of tracking.

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...

AI Technical Summary

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: the traditional target tracking method generally uses the Euclidean distance to calculate the similarity of two regions

The weight of the edges used to construct the static structure graph is based on the low-level feature distance calculation, and the Euclidean distance cannot accurately distinguish the target from the background

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