Video frequency behaviors recognition method based on track sequence analysis and rule induction

Abstract

The invention discloses a method for identifying the video action based on trajectory sequence analysis and rule induction, which solves the problems of large labor intensity. The method of the invention divides a complete trajectory in a scene into a plurality of trajectory section with basic meaning, and obtains a plurality of basic movement modes as atomic events through the trajectory clustering; meanwhile, a hidden Markov model is utilized for establishing a model to obtain the event rule contained in the trajectory sequence by inducting the algorithm based on the minimum description length and based on the event rule, an expanded grammar analyzer is used for identifying an interested event. The invention provides a complete video action identification frame and also a multi-layer rule induction strategy by taking the space-time attribute, which significantly improves the effectiveness of the rule learning and promotes the application of the pattern recognition in the identification of the video action. The method of the invention can be applied to the intelligent video surveillance and automatic analysis of movements of automobiles or pedestrians under the current monitored scene so as to lead a computer to assist people or substitute people to complete monitor tasks.

Description

technical field [0001] The invention belongs to the technical fields of artificial intelligence, computer vision, pattern recognition and machine learning, and in particular relates to a behavior recognition method in videos. Background technique [0002] With the rapid development of computer technology, the new high-performance central processing unit provides computers with powerful computing capabilities, and large-capacity memory and hard disks provide computers with super data storage and reading capabilities. However, in many aspects involving human perceptual activities, such as visual function, auditory function, olfactory function, natural language understanding function, etc., the computer is still far from the level of the human brain. This status quo cannot meet the needs of some advanced applications. For example, we hope that computers can analyze human behavior in visual surveillance scenarios. When abnormal behaviors such as someone climbing over the fence, ...

AI Technical Summary

Problems solved by technology

The main problem faced by this method at present is that the event rules cannot be obtained automatically. In most rule-based reasoning work, the event rules are manually specified, and domain experts need to set rules for all possible events of interest in a scene. , the labor cost is very large

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