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Frame Adaptive Object Tracking Algorithm Based on Sparse Representation Selective Appearance Model

A technology of sparse representation and appearance model, applied in the field of target tracking of computer vision, which can solve the problems of inaccurate use of image information, lack of adaptability to different frames, and high computational complexity

Active Publication Date: 2018-04-03
TIANJIN UNIV
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Problems solved by technology

However, this method is not adaptive to different frames, resulting in high computational complexity, and the use of image information is not accurate enough.
[0004] So far, no frame-adaptive target tracking algorithm based on sparse representation selective appearance model has been developed in the papers and documents published at home and abroad. Therefore, the invention content of this patent is original

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  • Frame Adaptive Object Tracking Algorithm Based on Sparse Representation Selective Appearance Model
  • Frame Adaptive Object Tracking Algorithm Based on Sparse Representation Selective Appearance Model
  • Frame Adaptive Object Tracking Algorithm Based on Sparse Representation Selective Appearance Model

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[0046] The technical solution of the present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0047] The frame adaptive target tracking algorithm based on the sparse representation selective appearance model of the present invention is implemented by a tracker composed of two sub-trackers cascaded, including a discriminative tracker SDC and a generative tracker SGM. First, the tracker is initialized , store the appearance information of the target, obtain the template library A of the discriminative tracker SDC, and the dictionary D of the generative tracker SGM from the first frame. When a new frame of image arrives, multiple candidate targets are randomly sampled around the target position of the previous frame to form X. For each candidate target, the template library A of the discriminative tracker is run to solve the sparse representation coefficient α of the template library, and the weight struct...

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Abstract

The invention discloses a frame adaptive target tracking algorithm based on a sparse representation selective appearance model, which initializes a tracker and randomly samples multiple candidate targets; runs a discriminative tracker SDC; detects frame characteristics; runs a generative tracker SGM; updating the occlusion detection threshold Th; and computing a joint model and determining tracking. Compared with the prior art, the tracker proposed by the present invention can adapt to various scenarios and has wide application prospects. While reducing the computational complexity, the tracker can accurately use image information to make the tracking effect more accurate.

Description

technical field [0001] The present invention relates to the field of object tracking of computer vision, and more particularly, relates to a frame-adaptive object tracking algorithm using a sparse representation selective appearance model. Background technique [0002] As a cutting-edge subject of computer vision, object tracking technology is a research hotspot in both scientific and engineering fields. The task of target tracking is to obtain the position information of the target of interest from each image frame, including coordinates, size, rotation angle and even speed information, to realize the analysis and understanding of the object's trajectory, so as to achieve more advanced functions. There are many problems to be solved in target tracking, such as target occlusion, rotation, scale change, brightness change, etc. Therefore, the appearance representation method of tracking target is an important research topic in this field. [0003] Sparse representation is a r...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/223G06T7/136
Inventor 周圆田宝亮陈莹冯丽洋侯春萍
Owner TIANJIN UNIV
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