Video moving target classification and identification method based on outline constraint

Abstract

The invention provides a video moving target classification and identification method based on outline constraint. The video moving target classification and identification method includes the steps: (1) obtaining a realistic target region and a target outline through a level set partitioning algorithm which is based on color features, textural features and shape prior constraint; (2) conducting convolution operation on the realistic target region through Gaussian filter and obtaining space detail constituent of the target; (3) extracting a local binary pattern histogram of the space detail constituent and obtaining the textural features of the target; (4) extracting a directional gradient histogram of an outline constraint local region in the realistic target region and obtaining the edge gradient features of the target; (5) extracting the texture features and the edge gradient features of a training sample target, training the texture features and the edge gradient features of the training sample target through a machine learning method, obtaining a target classification model; and (6) extracting the texture features and the edge gradient features of a to-be-identified target, inputting the classification model and confirming the type of the target. By means of the video moving target classification and identification method based on outline constraint, classification accuracy under complex outdoor conditions is improved.

Description

Technical field [0001] The invention relates to a target recognition method, in particular to a video moving target classification and recognition method based on contour constraints for remote outdoor monitoring, belonging to the field of pattern recognition. Background technique [0002] With the continuous promotion of surveillance systems, video data is showing explosive growth. It is difficult to play the real-time and active supervision role of surveillance systems by relying solely on manpower. In order to solve the problem of low efficiency of video surveillance and excessive dependence on manual labor, intelligent processing technology has attracted widespread attention in academic research and engineering applications. [0003] Moving target classification and recognition, as a key technology of intelligent processing, refers to the recognition of the categories of moving targets in video sequences. The basic process of recognition is to train target classifiers based on ...

AI Technical Summary

Problems solved by technology

The texture feature of the image is the intrinsic feature of the image related to the target surface structure and material. As a simple and effective local texture description operator, LBP is used to describe the apparent model of the target texture feature. By describing the neighborhood gray space of the pixel The distribution describes the texture of the target, which has the advantage of fast calculation speed, but its robustness is easily affected by illumination changes and local noise.

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