Object prediction area optimization method applicable to target identification

Abstract

The invention discloses an object prediction area optimization method applicable to target identification, comprising the following steps: expanding an object prediction area; performing image segmentation; performing super pixel bounding box calculation and super pixel significance evaluation; and finally, getting an optimized object prediction area based on sliding window search of super pixels. By controlling the size of super pixels, the scale of image segmentation is increased, the search scope of super pixels is reduced, and therefore, the time complexity of the algorithm is low and is only related to the number of super pixels in an image; as the pixels in the super pixels are consistent and segmentation of the local edge is better, a better locating effect can be produced by taking super pixels as the basic elements of sliding window search compared with single pixels; the search area is reduced effectively, and the traditional pixel-based sliding window search algorithm is accelerated; and in addition, by cascading the method, the target identification accuracy of the existing target identification algorithm can be improved.

Description

【Technical field】 [0001] The invention belongs to the field of image processing and computer vision, and in particular relates to an object prediction region optimization method suitable for target recognition. 【Background technique】 [0002] Vision is an important way for human beings to obtain external information, and images are an important carrier of information. With the development of image processing technology, the size and resolution of images are gradually increasing, and the information contained in them is also constantly enriched. Studies have shown that when humans observe an image, their eyes usually move between the objects contained in the image, and they are not interested in other areas such as the background. Most visual technologies, such as pedestrian detection, face recognition, target tracking and target recognition, etc. , which also acts on the above-mentioned region containing the object. Therefore, how to quickly and effectively locate the posi...

AI Technical Summary

Problems solved by technology

However, the method based on sliding window search mainly faces two problems in practical application: 1. Since the entire image needs to be searched and the classifier is applied in all possible positions, the time complexity of the algorithm is relatively high; 2. How to effectively train Classifiers with position discriminative features still need further research

For the first problem, most of the existing improvements use heuristic algorithms to speed up the search process, but it will increase the probability of false detection of object areas

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