Method for detecting object in depth image

Abstract

The present invention relates to a method for detecting an object in a depth image. The method comprises the following steps: for an input depth image, establishing a depth tree model based on a multi-way tree data structure and a mapping diagram of a depth tree node and a depth image pixel; and traversing each node of the depth tree model, making statistics and analysis on node data, and determining a node attribute, so as to obtain a rectangular region where an object is located. According to the method provided by the provided by the present invention, the rectangular region of the object can be rapidly and accurately obtained, accuracy of object detection in the depth image can be improved, so that a detection result can not only meet human visual requirements but also relatively well meet the demands for speed and precision in practical application.

Description

technical field [0001] The invention relates to an object detection technology in the field of computer vision, in particular to an object detection method in a depth image. Background technique [0002] Object detection has always been a basic problem in the field of computer vision, and it is also a difficult problem. Obtaining as few object regions as possible and not related to the object category is the core of object detection. Traditional methods are mostly based on rectangular frames, that is, a series of rectangular frames are used to frame objects in the image as tightly as possible. This type of method is widely used in the field of traditional two-dimensional images, and has achieved good detection accuracy while taking into account the speed. However, this method also has problems such as generating more candidate boxes and low compactness of the candidate boxes. . Another method is based on region segmentation, which is to segment out object regions with spec...

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Problems solved by technology

This type of method is widely used in the field of traditional two-dimensional images, and has achieved good detection accuracy while taking into account the speed. However, this method also has problems such as generating more candidate boxes and low compactness of the candidate boxes.

Another method is based on region segmentation, which is to segment out object regions with specific practical significance in the image. This type of method generally has high accuracy, but the algorithm complexity is high and the speed is generally slow.

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