Abnormal behavior description method based on characteristics of block mass and track

Abstract

The invention provides an abnormal behavior description method based on characteristics of a block mass and a track. The abnormal behavior description method comprises the following steps of utilizing a characteristic extraction method to extract color characteristics, textural characteristics and position characteristics of a scene, utilizing a K-means algorithm to cluster the characteristics to generate the block mass, and utilizing a conditional random fields (CRF) to conduct description on the block mass; and utilizing a mixed gaussian model to conduct sport target detection, extracting track characteristics of a target, combining the block mass description and the track characteristics to form a combined characteristic vector quantity, using a hidden markov model (HMM) to conduct modeling on the combined characteristic vector quantity, utilizing the built HMM to describe abnormal behaviors of the target in the scene, and enabling an abstract attached diagram to serve as a functional block diagram of the abnormal behavior description method . The abnormal behavior description method based on the characteristics of the block mass and the track not only considers influences of the scene on the abnormal behaviors, but also achieves long-time tracking and describing on the target.

Description

technical field [0001] The present invention relates to the fields of video signal processing, pattern recognition, and intelligent monitoring. Specifically, the present invention relates to a method for describing abnormal behavior based on the characteristics of clumps and trajectories. Background technique [0002] Abnormal behavior description has broad application prospects in the fields of intelligent video surveillance, video annotation, virtual reality, human-computer interaction, public security, etc. Driven by these applications, abnormal behavior description has become a research hotspot in the fields of computer vision and pattern recognition . [0003] The template sequence matching method and the feature sequence matching method are traditional abnormal behavior description methods. The matching method has a small amount of calculation, but it is sensitive to the time interval of the target, and the samples of abnormal behavior are rare, so it can only describe...

AI Technical Summary

Problems solved by technology

[0004] However, the above methods are all based on the underlying state space characteristics of the target, and do not consider the influence of the scene where the target is located.

Moreover, traditional state-space methods are based on single-frame images or several or dozens of frames of images, which belong to short-term behavior and do not consider long-term behavior

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