Pedestrian individual segmenting method and device

Abstract

The invention discloses a pedestrian individual segmenting method and device. The method includes: utilizing a thick-granularity human shape profile segmenting model which is well trained in advance to perform pedestrian segmenting on a to-be-processed image to acquire a blocked segmenting result which includes multiple blocks marked as background and foreground, wherein the blocks marked as the background in the to-be-processed image do not include pedestrian subject, and the blocks marked as the foreground include part of images of the pedestrian subject; removing a background image in a part corresponding to the blocked pedestrian segmenting result in the to-be-processed image to acquire a thick-granularity segmenting image; inputting the thick-granularity segmenting image into a thin-granularity human shape profile segmenting model which is trained well in advance; outputting an individual pedestrian segmenting result by the thin-granularity human shape profile segmenting model which is well trained in advance, wherein each of the thick-granularity human shape profile segmenting model and the thin-granularity human shape profile segmenting model is acquired by training through a full-convolution neural network.

Description

technical field [0001] The invention relates to the technical fields of computer vision and pattern recognition, in particular to a pedestrian individual segmentation method and device based on the combination of thickness and granularity of a fully convolutional neural network. Background technique [0002] Pedestrian individual segmentation is one of the most important problems in the fields of scene understanding and biometric recognition. Most traditional pedestrian segmentation methods require that the background cannot contain other pedestrians, and the segmentation results are obtained by distinguishing the difference between the human body and the environment. However, there are a large number of situations where pedestrians occlude each other in actual scenes. At this time, traditional pedestrian segmentation methods cannot obtain satisfactory results. The combination of individual detection and pedestrian segmentation can partially solve this problem, but individu...

AI Technical Summary

Problems solved by technology

However, there are a large number of situations where pedestrians occlude each other in actual scenes. At this time, traditional pedestrian segmentation methods cannot obtain satisfactory results.

The combination of individual detection and pedestrian segmentation can partially solve this problem, but individual detection is time-consuming, and in many cases even if the individual position is accurately detected, it is still impossible to obtain a perfect individual due to the fact that the area contains multiple human body information. Segmentation result

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