Layered stack based violent group behavior detection method

Abstract

The invention provides a layered stack based violent group behavior detection method. The method is characterized by comprising the steps of preprocessing an original monitoring video; performing block segmentation on the preprocessed monitoring video and extracting a space-time invariant feature for each video block; selecting a training sample for feature quantification to perform training to obtain a video dictionary; quantifying features of to-be-detected samples by utilizing the video dictionary obtained by training; selecting the quantified features as training samples of a support vector machine (SVM) classifier to train the SVM classifier; and classifying the to-be-detected samples by utilizing the trained SVM classifier and judging whether a to-be-detected video contains a violent group behavior or not. Compared with other similar methods, the detection method has the characteristics that the speed is higher, the accuracy is higher, and the features are more differentiable; and in addition, most violent behaviors and normal behaviors can be distinguished in violent group behavior detection, so that the capability of computer assisted detection analysis is effectively improved.

Description

technical field [0001] The invention relates to the field of group behavior recognition, in particular to a hierarchical cascading-based violent group behavior detection method using hierarchical features and dictionary learning. Background technique [0002] In recent years, various security incidents have occurred frequently in public areas at home and abroad. In these security incidents, violent acts such as stampedes and fights have occurred frequently, which has brought extremely serious impacts on social security. In large public places, such as opera houses, gymnasiums, railway stations, subway stations, etc., the safety guarantee is extremely important. In order to ensure the safety of these places, video surveillance cameras are widely used in daily security. However, the effect of video surveillance cameras is very limited. In most cases, these cameras have not been fully utilized. The main reason is that there is a lack of a reasonable automated video analysis met...

AI Technical Summary

Problems solved by technology

[0007] 2) Traditional action feature descriptors cannot effectively represent the characteristics of crowd movement

[0008] 3) The existing feature descriptors cannot meet real-time or quasi-real-time requirements. Some classic features such as MoSIFT (MotionScaleInvariantFeatureTransform) spatio-temporal features, although good detection results are obtained, but their extraction speed is very slow and cannot be put into the actual production environment.

From the above analysis, it can be seen that there is currently a lack of an algorithm that can efficiently and detect violent group behavior in real-time or quasi-real-time

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