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A feature detection method based on sparse coding

A feature detection and sparse coding technology, which is applied in the fields of computer vision and target tracking, can solve the problems of time-consuming offline training of neural network models, and achieve the effects of improving representation ability, accuracy and robustness, and accuracy

Active Publication Date: 2019-01-18
HARBIN INST OF TECH
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But neural network models often require a time-consuming offline training process

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  • A feature detection method based on sparse coding
  • A feature detection method based on sparse coding
  • A feature detection method based on sparse coding

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[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0076] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0077] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but not as a limitation of the present invention.

[0078] Such as figure 1 Shown, the feature detection method based on sparse coding of the present invention, de...

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Abstract

In order to improve the accuracy of target tracking, the invention provides a feature detection method based on sparse coding, which belongs to the technical field of target tracking in the field of computer vision. The invention comprises the following steps: S1, extracting the local feature points by using the FAST corner detection algorithm based on the local threshold, calculating the local gradient direction of the feature points, sampling an image block centered on the feature points as a training sample; S2, grouping the dictionary elements according to the local gradient direction of the feature points to obtain an over-complete dictionary; S3, using the obtained dictionary to carry out sparse representation of the test sample, and then dividing the image block into blocks to construct sparse features, and realizing target tracking according to the detector of the sparse features. The invention improves the accuracy and robustness of target tracking by utilizing the sparse characteristics of the sparse encoding learning target. The dictionary elements of different groups are trained according to the local gradient direction of the image block to reflect the local directioninformation of the image block and to improve the accuracy of target tracking.

Description

technical field [0001] The invention relates to a target feature detection method, in particular to a feature detection method for learning target sparse features based on sparse coding, and belongs to the technical field of target tracking in the field of computer vision. Background technique [0002] Algorithms for general object tracking have made great progress in recent years. The detection-based tracking framework (tracking-by-detection) has achieved a series of successes in the field of object tracking by combining image detection and existing tracking techniques. Benefiting from the high efficiency and accuracy of detection algorithms, detection-based tracking frameworks tend to have better accuracy and efficiency than existing motion estimation-based tracking algorithms. The existing feature detection algorithm contains rich local feature information, which can fully represent the target and has a small amount of calculation, which is helpful to improve the perform...

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Application Information

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IPC IPC(8): G06K9/46G06K9/62
CPCG06V10/44G06F18/2136G06F18/2411G06F18/214
Inventor 贾敏高政郭庆顾学迈刘晓锋
Owner HARBIN INST OF TECH
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