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Behavior recognition method based on multi-instance markov model

A Markov model and recognition method technology, applied in character and pattern recognition, instruments, computer parts, etc., to achieve the effect of reducing annotation

Inactive Publication Date: 2014-01-29
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem of behavior recognition in complex scenes. To this end, the invention provides a behavior recognition method based on a multi-instance Markov model

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  • Behavior recognition method based on multi-instance markov model
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  • Behavior recognition method based on multi-instance markov model

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Embodiment Construction

[0019] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0020] figure 1 It is a flow chart of the behavior recognition method based on the multi-instance Markov model proposed by the present invention, such as figure 1 As shown, the described behavior recognition method based on multi-instance Markov model comprises the following steps:

[0021] Step S1, establish a training set, the training set contains positive and negative sample videos of a certain action A; extract its local spatiotemporal interest points for each video in the training set; perform the first aggregation on the extracted spatiotemporal interest points Class, get these spatio-temporal interest points corresponding to the category of the first cluster center;

[0022] For example, clustering the extracted...

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Abstract

The invention discloses a behavior recognition method based on a multi-instance markov model. The behavior recognition method includes the following steps that local features of each video are abstracted and a feature histogram of a local video block is used for expressing certain local movement of behavior; a random sampling manner is used for obtaining multiple local video blocks, the local video blocks form multiple markov chains and the markov chains are expressed as continuous movement of some local movement in time; under a frame of multi-instance study, the model selects the markov chain with the most recognition performance to express behavior. In the time of testing, multiple markov chains are composed in the same manner to express the videos and the scores of the markov chains are calculated. If the scores of the markov chains are larger than a certain threshold value, the markov chains belong to the behavior, and otherwise, the markov chains do not belong to the behavior. According to the multi-instance markov model, the aim of behavior recognition under complicated scenes is achieved and labels of the videos are reduced.

Description

technical field [0001] The invention belongs to the technical field of intelligent video monitoring, and in particular relates to a behavior recognition method based on a multi-instance Markov model. Background technique [0002] Behavior recognition has a wide range of applications in video intelligent surveillance. For example, in some specific occasions, airports, squares, streets, shops, etc., the early warning of dangerous behaviors of individuals and groups has important application value for public safety. At present, the mainstream behavior recognition is mainly through the construction of word bags based on local features. This method does not consider the distribution information and semantic information of behavioral feature points in time and space, and these local feature detectors detect many noisy feature points from the background. In order to solve these problems, Sadanand et al. proposed a new behavior representation method, which incorporates semantic in...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 王春恒周文肖柏华张重
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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