Selective feature background subtraction method aiming at thick crowd monitoring scene

Abstract

The invention provides a selective feature background subtraction method aiming at a thick crowd monitoring scene. Aiming at a problem that a traditional background subtraction method can cause higher missed inspection and error inspection rate under a thick crowd scene, the invention discloses the selective feature background subtraction method, which comprises the following steps of: creating a scene sparsity model; selecting a video frame having higher similarity to the sparsity model as a training sample, and obtaining an initialized feature scene based on batching principal component analysis; updating the scene sparsity model, selecting a video frame having higher similarity to the sparsity model to update the feature background by incremental principal component analysis; selectively rebuilding the background at pixel level; and solving an adaptive threshold to threshold a difference image and obtain a foreground image. The selective feature background subtraction method can inspect out slowly moving and static foreground objects well in the thick crowd scene with relatively steady light and simultaneously keep lower error inspection rate.

Description

technical field [0001] The invention relates to an image and video processing method, in particular to a selective feature background subtraction method for crowded monitoring scenes. Background technique [0002] Traditional surveillance video analysis adopts the way of human eyes watching. However, with the wide spread of video surveillance systems today, the traditional human-based solutions are faced with the realistic problem of having to solve very large video data. Therefore, the necessity of intelligent video surveillance becomes more and more obvious. Relying on computer vision analysis technology, the intelligent video surveillance system detects and tracks objects appearing in the camera scene by separating the background and objects in the scene. Users can preset different event rules in different monitoring scenarios and applications. Once the target violates the predefined rules in the scene, the system will detect it as an abnormal event and automatically is...

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Problems solved by technology

[0007] From the above analysis, it can be seen that due to the special characteristics of cr

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