Superpixel substantial object detection algorithm based on area energy

Abstract

The present invention provides a superpixel substantial object detection algorithm based on the area energy. The algorithm comprises: decoding the input video information to be observed to be an independent image frame sequence in a RGB format; setting a substantial algorithm parameter, and extracting a substantial area through the substantial algorithm; setting an area energy algorithm parameter, determining a pre-estimation energy threshold value of a horizontal direction and a pre-estimation energy threshold value of a vertical direction, dividing the target area threshold value of the substantial area, and extracting the substantial area of the substantial detection object; calculating the energy concentration degree of the energy substantial area; setting a superpixel segmentation input parameter , performing superpixel segmentation of the image frame, and obtaining a superpixel segmentation diagram; and according to the energy concentration degree, extracting the superpixel areas satisfying the energy concentration degree to form a final substantial object result and realize a substantial object detection process. The superpixel substantial object detection algorithm based on the area energy can effectively eliminate the background information and extract the observation target so as to provide more effective characteristic information criteria for the target tracking and identification.

Description

technical field [0001] The invention belongs to the technical field of salient target detection, in particular to a superpixel salient target detection algorithm based on area energy. [0002] technical background [0003] As a basic technology in the field of video processing, salient object detection technology has been widely used in many fields of computer vision. At present, the salient object detection technology mainly determines the salient object by constructing a visual saliency model for saliency analysis. Schauerte et al. proposed to use saliency to build a bottom-down model, simulate the human visual mechanism, use a zooming and rotating camera to detect salient objects with directionality, and introduce a learning mode for scale-invariant features to continuously calibrate the salient object detection results. Jun-Yan, Zhu et al. proposed an unsupervised target detection method, which transformed unsupervised learning into multi-instance learning through salien...

AI Technical Summary

Problems solved by technology

[0004] Although the existing salient target detection technology can obtain the target position, but because the detection result contains non-subject background redundant information, the recognition range of contour and texture features is enlarged, or only a part of the target is detected, and a large number of important features are lost and the recognition area is reduced. , these inaccurate target features cannot provide accurate target parameter information for subsequent processing, resulting in the failure of calculation processes such as target recognition, target tracking, pedestrian detection, and behavior analysis.

For the existing salient object detection technology, the main problem is that it cannot accurately extract the contour and texture features, and can only obtain the approximate area of ​​the salient object, which contains a large amount of background redundant information. The optimization of the salient object detection technology mainly adopts more complex optimization. Learning algorithm to improve accuracy, but this greatly reduces the processing power of the algorithm and cannot detect targets in real time

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