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Multiple athletes behavior detection method based on scale adaptation local spatiotemporal features

A scale-adaptive, spatio-temporal feature technology, applied to computer parts, instruments, character and pattern recognition, etc., can solve problems affecting the accuracy of multiple players

Inactive Publication Date: 2016-09-21
GUANGXI UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

But multiple simultaneous events occur in the subset of features identified by team behavior
In special cases, behaviors with high priority set in advance may be unimportant behaviors, which affects the accuracy of multi-player behavior recognition

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  • Multiple athletes behavior detection method based on scale adaptation local spatiotemporal features
  • Multiple athletes behavior detection method based on scale adaptation local spatiotemporal features
  • Multiple athletes behavior detection method based on scale adaptation local spatiotemporal features

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Embodiment Construction

[0042] In order to clearly understand the technical solution of the present invention, its detailed structure will be presented in the following description. Obviously, the implementation of the embodiments of the invention is not limited to specific details familiar to those skilled in the art. The preferred embodiments of the present invention are described in detail below, and there may be other implementations besides those described in detail.

[0043]The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0044] In this paper, referring to LINDEBERG's method for adaptive selection of local scale features in space, the Harris detection operand is extended to the time-space domain of football match video, and a scale-adaptive local spatio-temporal feature detection algorithm (1) is proposed, which defines a The difference operand that can obtain the maximum value in both time and space dimensions use...

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Abstract

The invention discloses a multiple athletes behavior detection method based on scale adaptation local spatiotemporal features, and the method comprises the following steps: S1, a normalized Laplace operand is adopted to estimate a local scale and establish a scale adaptation local spatiotemporal feature detection algorithm, and a Harris spatiotemporal interest point detection operand and the Laplace operand are combined to infer a Harris-Laplace spatiotemporal interest point detection operand; S2, the normalized Laplace operand is generalized to a spatiotemporal domain; S3, local spatiotemporal feature descriptors are generalized to three-dimensional soccer match video images; then before a K-means clustering algorithm is adopted to generate a spatiotemporal codebook, each spatiotemporal interest point is normalized to ensure that the each spatiotemporal interest point is invariable under scaling and shifting.

Description

technical field [0001] The invention belongs to the field of behavior recognition, and in particular relates to a multi-player behavior detection method based on scale-adaptive local spatio-temporal features. Background technique [0002] The quality of multi-player behavior representation in football game video directly affects the accuracy of behavior recognition. In the process of multi-player behavior analysis and recognition in football matches, the more features selected, the more fully the description of the behavior will be. However, selecting too many features will lead to excessive redundancy among the data, the dimension of the feature vector is too high, the essential law of data distribution is not easy to be found, the amount of data required to train the behavior recognition model is too large, and the calculation of the algorithm is too large. It is not conducive to the real-time processing of behavior recognition. Therefore, the key issue in the research o...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V20/42G06V20/44G06V20/53
Inventor 王智文蒋联源王宇航
Owner GUANGXI UNIVERSITY OF TECHNOLOGY
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