Compression tracking method and system based on surf

A technology of compressed tracking and compressed sensing, which is applied in SURF-based compressed tracking methods and systems, can solve problems such as easy tracking failures, and achieve the effects of small computational time complexity, high extraction accuracy, and fast feature extraction

Active Publication Date: 2017-10-31
SHENZHEN UNIV
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Problems solved by technology

Kaihua Zhang et al. proposed compressed tracking based on the theoretical knowledge of compression. The tracking of the target meets the general real-time requirements. Since the haar-like features of the image are extracted, when the target has partial occlusion, scale transformation, rotation, and illumination changes, etc. interference , easy to track failure

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  • Compression tracking method and system based on surf
  • Compression tracking method and system based on surf
  • Compression tracking method and system based on surf

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[0048] In order to make the object, technical solution and effect of the present invention more clear and definite, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0049] The Speeded-Up Robust Features (SURF) algorithm is an image local feature inspection algorithm. When the target image undergoes scale transformation, rotation, illumination and other environmental changes, the algorithm is still robust and has high calculation accuracy. . The SURF algorithm can be divided into two parts: feature point detection and feature description factor formation. figure 1 It is a schematic diagram of the steps of the SURF algorithm, as shown in the figure: in the feature point detection part, the image integral map is used to improve the calculation efficiency,...

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Abstract

The SURF-based compression tracking method and system provided by the present invention use SURF to extract image features and compressive sensing by collecting the positive samples and negative samples of the t-th frame image and the t-N-th frame to the t-th frame image of the video file respectively. technology, obtain the first classifier and the second classifier, and weight the two to obtain the third classifier; use the third classifier to track the target image in the t+1th frame image of the video file. The target tracking method and system of the present invention, because SURF is used to extract image features, the feature extraction speed is fast, and the image features can still be correctly extracted when the target environment changes in scale, rotation, etc. The feature is compressed, so the processed data is less, and the update speed of the classifier is improved, so the target tracking method can accurately track the target in a complex environment, and has real-time and robustness.

Description

technical field [0001] The present invention relates to the technical field of computer vision, in particular to a SURF-based compression tracking method and system. Background technique [0002] Object tracking refers to dynamically locating the position of the moving object of interest in the video image sequence. The object of interest should have some significant image features, such as texture, color, shape, space and other image information. During the target tracking process, the behavior trajectory of the target of interest can be monitored, and reliable data information can be provided for further analysis of the target of interest. [0003] Object tracking is an important research direction in the field of computer vision technology, and it is often used in video security monitoring, traffic control, artificial intelligence, human-computer interaction and other fields. In military applications, it has been widely used in military reconnaissance, air target surveil...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/246
Inventor 陈汉准张力胡媛媛
Owner SHENZHEN UNIV
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