Public scene intelligent video monitoring method based on vision saliency and depth self-coding

Abstract

The invention relates to a public scene intelligent video monitoring method based on vision saliency and depth self-coding. The method includes performing single-frame decomposition on a video, using visual saliency to extract motion information, then calculating optical flow of a moving object in adjacent frames, dividing a later detection process into a training process and a testing process, during training, using optical flow of a training sample as input of self-coding, training a whole self-coding network through minimization of a loss function, in the test stage, using optical flow of the training sample and a test sample as input, extracting a coder in the trained self-coding network, extracting features of the input through dimensionality reduction, then visualizing a result after dimensionality reduction, utilizing a suprasphere to represent a visual range of the training sample, when the test sample is input, utilizing the same method to realize visualization, and if a sample visualization result falls in a suprasphere range, judging the sample to be normal; and otherwise, if the sample visualization result falls beyond the suprasphere range, judging the sample to be abnormal, thereby realizing intelligent monitoring of the video.

Description

technical field [0001] The invention relates to image processing technology, in particular to an intelligent video monitoring method for public scenes based on visual salience and deep self-encoding. Background technique [0002] In recent years, monitoring equipment has been used in all walks of life. Modern airports, stations, hospitals and other public scenes have covered thousands of monitoring devices. Due to the large amount of video data, security personnel analysis alone can filter out normal Behavior, discovering abnormal behavior in time is a huge workload, and with the increase of the number of analysis, the attention and work efficiency of personnel will be significantly reduced. In order to liberate people from a large number of analysis and understanding, a research An intelligent video surveillance method is of great significance. [0003] The intelligent monitoring system mainly involves three parts: the extraction of motion information in the video, that is...

AI Technical Summary

Problems solved by technology

[0002] In recent years, monitoring equipment has been used in all walks of life. Modern airports, stations, hospitals and other public scenes have covered thousands of monitoring devices. Due to the large amount of video data, security personnel analysis alone can filter out normal Behavior, discovering abnormal behavior in time is a huge workload, and with the increase of the number of analysis, the attention and work efficiency of personnel will be significantly reduced. In order to liberate people from a large number of analysis and understanding, a research An intelligent video surveillance method is of great significance

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