Method and equipment for common detection of human faces and human bodies

Abstract

The invention discloses a method and equipment for common detection of human faces and human bodies. The method comprises the steps that standard data is obtained, wherein the standard data comprises position frame information for labeling the positions of the human faces and the human bodies of all pedestrians; a common recognition model is corrected through the position frame information in the standard data, wherein the common recognition model is generated based on Faster RCNN; common detection of the human faces and the human bodies is carried out on images in a video to be recognized through the corrected common recognition model to output real-time structuralization information, wherein the structuralization information comprises human face information and human body information of the pedestrians at the same time. Thus, efficient simultaneous recognition is achieved, resources are saved, practicality is improved, and the corresponding relation between the human faces and the human bodies is guaranteed.

Description

technical field [0001] The invention relates to the field of recognition, in particular to a method and equipment for joint detection of human faces and human bodies. Background technique [0002] In the prior art, face recognition and human body recognition are performed separately, and only a single target type can be detected. If multiple types of targets are detected, multiple independent models are required. For example, when it is necessary to detect faces and To detect pedestrians, you need a face detector and a human detector. Using two independent detectors will take up more resources. [0003] In addition, the detection of the target and the correction of the detection results are carried out separately, and it is necessary to repeatedly classify and screen the candidate frames and correct the position and size of the candidate frames. There are a lot of repeated calculations, which reduces the practicability of the system; [0004] In the follow-up process of rea...

AI Technical Summary

Problems solved by technology

[0002] In the prior art, face recognition and human body recognition are performed separately, and only a single target type can be detected. If multiple types of targets are detected, multiple independent models are required. For example, when it is necessary to detect faces and To detect pedestrians, you need a face detector and a human detector. Using two independent detectors will take up more resources

[0003] In addition, the detection of the target and the correction of the detection results are carried out separately, and it is necessary to repeatedly classify and screen the candidate frames and correct the position and size of the candidate frames. There are a lot of repeated calculations, which reduces the practicability of the system;

[0004] In the follow-up process of real-time information structuring, the independently detected face and human body lack a corresponding relationship, but in actual needs, it is necessary to map both the face and the human body to a specific individual target, based on the existing face To do the detection and human detection results, it takes extra calculations to match

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