Gait data generation method and device based on adversarial network

Abstract

The embodiment of the invention provides a gait data generation method and device based on an adversarial network. The method comprises the steps of obtaining a plurality of first gait picture sets ofa target object, inputting the plurality of first gait picture sets into a generation network to obtain a plurality of second gait picture sets, inputting a third gait picture set and the second gaitpicture sets into a first adversarial network for comparison, and obtaining a second adversarial network if a comparison result is smaller than a preset threshold value, determining that the generation network is a practical generation network; and acquiring gait data of any plurality of gait picture sets of the target object by adopting the second adversarial network and the practical-level generation network. According to the gait data generation method based on the adversarial network provided by the embodiment of the invention, the gait sequence graph of the target object can be supplemented under the condition that only a small number of pictures exist in the collected gait graph; a good compensation effect is achieved on collection and labeling of a large number of gait samples, andthe economic cost is reduced.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of pattern recognition, and in particular to a method and device for generating gait data based on an adversarial network. Background technique [0002] At present, in the field of artificial intelligence security applications, face recognition is very popular, but it is affected by factors such as wearing masks, hats, and sunglasses. Once the camera does not capture the facial features of the suspect, or the captured facial features are compared Ambiguity often causes greater difficulties in case detection. Gait recognition aims to identify people through their walking posture, which has the advantages of non-contact, long-distance and not easy to camouflage. However, the gait recognition model needs more labeled samples as support in the training process to train a better model, and it is often difficult to collect and label a large number of samples, so how to quickly obtain valuabl...

AI Technical Summary

Problems solved by technology

[0002] At present, in the field of artificial intelligence security applications, face recognition is very popular, but it is affected by factors such as wearing masks, hats, and su

Images