Automatic focusing method for human face region in high-magnification shallow depth-of-field state

Abstract

The invention provides an automatic focusing method for a human face region in a high-magnification shallow depth-of-field state, which can perform human face detection, region optimization and automatic focusing in a camera product with insufficient computing power in a high-magnification shallow depth-of-field scene so as to find the clewest Focus motor position of a speaker region. The method comprises the following steps: 1, training a face detector based on a YOLO V3 neural network; 2, calculating a human face region set in the image by using a human face detector; 3, optimizing a human face region; 4, optimally selecting a face region to be matched with the region divided by the chip, and locking an interested focusing region; 5, calculating a region of interest; and 6, performing automatic focusing.

Description

Technical field [0001] The invention relates to a method for automatically focusing a face area in a state of large magnification and shallow depth of field, which is applied in the field of cameras for educational recording and broadcasting and video conferences. Background technique [0002] In recent years, in applications such as educational recording and broadcasting, video conferencing, zoom cameras such as 10x, 12x, or 20x are usually used to play real-time videos with characters as the subject in an indoor scene of 5-10 meters. The key point is to ensure that the character's face area is absolutely clear. In this context, it is proposed to use the face area as the key area of ​​autofocus to effectively solve this problem. [0003] Considering such a usage scenario, such as figure 1 As shown, in this three-dimensional scene, the background (blackboard, background wall, etc.), subject (speaker), and camera (10 times, 12 times, or 20 times) are in order from far to near. For...

AI Technical Summary

Problems solved by technology

[0004] (1) The depth of field will become shallow when the zoom lens is pulled to a large magnification, and it is impossible to ensure that the background and characters are clear at the same time

[0005] (2) The strategy of reducing noise and increasing the aperture will lead to a shallower depth of field, which cannot ensure that the background and characters are clear at the same time

[0006] (3) The background details are too rich, which is mistaken for the subject of the image in the autofocus algorithm and a false peak is calculated, resulting in a clear background and blurred characters

[0007] (4) The details of the characters are too single, and the weight in the auto-focus algorithm is too small, which is ignored by the algorithm when calculating FV (Focus Value), resulting in clear distances from other objects and blurred characters

However, in the face of well-designed, compact and cost-effective camera products, part of the computing power is often consumed in terms of imaging requirements, and then embedding face detection and area designation algorithms into the system will inevitably cause a shortage of computing power, making it difficult to achieve real-time processing.

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