Abnormity detection method based on deep learning in complex environment

Abstract

The invention provides an abnormity detection method based on deep learning in complex environment. An object space-time characteristic extracted through a convolution neural network regression method is input into an LSTM model, and then motion trajectories of multiple objects in the complex environment are tracked; non-linear space-time actions of adjacent individuals are captured in a case of irregular movements of the multiple objects, dependence of the motion trajectories between the adjacent individuals is evaluated, and future motion trajectories of the individuals are predicted; and abnormity detection is completed according to abnormity probabilities of the future motion trajectories of the individuals. The method can reduce the false detection rate of images. In the prior art, a space-time characteristic of a single object is mainly detected without considering a mutual interference condition existing between the motion trajectories of the adjacent individuals in the complex environment. According to the LSTM model, the dependence between the several individuals is evaluated, and the future motion trajectories of the objects are predicted by using a coding and decoding framework, so an accurate result can be obtained when abnormity detection is performed on movements of the multiple objects.

Description

technical field [0001] The invention relates to an anomaly detection method based on deep learning in a complex environment. Background technique [0002] In general, anomaly detection refers to the detection of abnormal behavior in the environment or data that does not conform to expected behavior. With the promotion of deep learning in the field of artificial intelligence, computer vision technology has been widely used in anomaly detection in complex environments such as subways, stadiums, and airports. However, such high-density environments have brought great challenges to anomaly detection. . In the face of the continuous irregular movement of a large number of objects, how to solve the problem of mutual interference between objects and how to detect anomalies under the condition that the motion trajectories of multiple objects influence each other have become an important problem in the current anomaly detection research. [0003] Abnormalities can be rare shapes or...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide an anomaly detection method based on deep learning in a complex environment, which can reduce the false detection rate of images under the condition that adjacent individual motions affect each other, especially in a crowded environment. Good performance provides a new idea for solving the problem of anomaly detection in complex environments, and solves the existing technology that does not consider the mutual interference of adjacent individual motion trajectories in complex environments, making the anomaly detection effect unsatisfactory The problem

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