Target object occlusion detection method and target object occlusion detection device

Abstract

The invention provides a target object occlusion detection method and a target object occlusion detection device. The target object occlusion detection method comprises steps: input images are received; a well-trained neural network is used to detect a target image in the input images and output a thermodynamic chart of the target object; and based on the thermodynamic chart, whether occlusion exists in the target object is detected. According to the target object occlusion detection method and the target object occlusion detection device provided by the embodiment of the invention, the target object in the input images is converted to the thermodynamic chart based on the well-trained neural network, whether the occlusion exists in the target object is detected through the thermodynamic chart, the target object recognition difficulty can be effectively reduced, and the recognition precision and the stability can be enhanced.

Description

technical field [0001] The present invention relates to the technical field of image processing, and more particularly to a method and device for detecting occlusion of a target object. Background technique [0002] For the recognition of an object in the image, the occlusion of the object in the image is a serious interference factor, such as masks, glasses, bangs, hats, decorations, etc. on the face of the face during face recognition. cause occlusion. On the other hand, in some applications, such as face recognition in surveillance video, there will be cases of intentional interference and confrontation recognition through occlusion. These will seriously affect the accuracy and stability of recognition. [0003] Due to the variety and inexhaustibility of occlusion methods and types, if a classifier is trained for each occlusion type, the workload will be huge and the amount of data will be severely constrained. Therefore, there is a need for a technology or system capa...

AI Technical Summary

Problems solved by technology

On the other hand, in some applications, such as face recognition in surveillance video, there will be cases of intentional interference and confrontation recognition through occlusion

These will seriously affect the accuracy and stability of recognition

[0003] Due to the variety and inexhaustibility of occlusion methods and types, if a classifier is trained for each occlusion type, the workload will be huge, and it will be severely constrained by the amount of data

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