Robustness prospect detection method based on multi-view learning

Abstract

The invention provides a robustness prospect detection method based on multi-view learning. The method includes the steps that a reference background image is acquired by an input video through time domain median filtering method, and iterative search and multi-scale fusion are conducted on a current image and the reference background image to acquire heterogeneous characteristics; conditional probability density of prospects and conditional probability density of backgrounds are calculated by using condition independence of the heterogeneous characteristics, and a prospect posteriori probability and a background posteriori probability are calculated by using Bayes rules according to prospect likelihood, background likelihood and a prior probability; an energy function of a Markov random field model is established by means of the prospect posteriori probability, the background posteriori probability and space-time consistency constraint, the energy function is minimized by using a belief propagation algorithm, and segmented results of the prospect and the background are obtained. By means of the method, in a complex challenging environment, robustness prospect detection is achieved.

Description

technical field [0001] The invention relates to intelligent video monitoring technology, in particular to a robust foreground detection method based on multi-view learning. Background technique [0002] Intelligent video surveillance is an important means of information collection, and foreground detection or background subtraction is a very challenging underlying problem in intelligent video surveillance research. On the basis of foreground detection, other applications such as target tracking, recognition, and anomaly detection can be realized. The basic principle of foreground detection is to compare the current image of a video scene with a background model and detect regions with significant differences. Although seemingly simple, foreground detection often encounters three challenges in practical applications: moving shadows, illumination changes, and image noise. Motion shadows are caused by light sources being occluded by foreground objects, which are hard shadows ...

AI Technical Summary

Problems solved by technology

Second, only the background model is established, and the foreground pixels are identified as outliers, and it is difficult to distinguish the foreground with similar color to the background

Third, no spatio-temporal consistency constraints in video sequences are exploited

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