Personnel behavior identification implementation system and method based on image segmentation and semantic extraction

Abstract

The invention relates to a personnel behavior identification and detection implementation system and method based on image segmentation and semantic characteristic extraction. The system comprises an image acquisition unit, a personnel behavior detection host computer, a user inquiry unit and an output interface unit. The method comprises the following steps: the personnel behavior detection host computer identifies personnel behaviors in the image data acquired by the image acquisition unit through image segmentation and image semantic characteristic extraction so as to generate personnel behavior presentation information. In the method, the personnel behavior detection host computer maps low-level features of an image into high-level semantics through a support vector machine so as to establish a mapping relation between the image and the image description, so that the content in the picture can be comprehended through digital image processing and analysis, the behaviors of personnel in the scene can be intelligently detected, and the identification accuracy of personnel behaviors in the image can be greatly improved. The system provided by the invention has a simple structure, and the method provided by the invention is simple and convenient to implement, low in application cost and wider in application range.

Description

technical field [0001] The present invention relates to the technical field of image information processing, in particular to the technical field of intelligent recognition of image information, and specifically refers to a system and method for realizing personnel behavior recognition based on image segmentation and semantic extraction. Background technique [0002] With the rapid development of computer technology and multimedia technology, the number of images has also increased greatly. How to quickly and accurately detect the required images from the massive image database has become a hot research issue in today's multimedia technology. Traditional text-based image retrieval technology requires administrators to manually annotate images, which not only consumes a lot of manpower, but also has a lot of subjectivity. For different administrators, the annotation results may be different. [0003] Content-Based Image Retrieval (CBIR) emerged as the times require, especiall...

AI Technical Summary

Problems solved by technology

Content-based image retrieval technology relies on low-level visual features (color, texture, shape, etc.) The "semantic gap" between the underlying visual features of the image and the semantics of the image, resulting in a distance between the semantics of the image and the actual meaning of the image, the recognition accuracy is low, and the efficiency of the content-based image retrieval method is low

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