Multi-view human body skeleton automatic labeling method based on OpenPose

Abstract

The invention discloses a multi-view human skeleton automatic labeling method based on OpenPose, belongs to the technical field of unmanned driving, and overcomes the defects of long time consumption,high cost, nonstandard labeling and the like due to the fact that most of existing public data sets are manually labeled. According to the invention, the collected multi-view data is labeled, and data reserve is provided for multi-view pedestrian action recognition model training. The method comprises the following steps: firstly, reading acquired multi-view video data, then performing pedestriandetection through an improved Yolov3 network, and filtering out pictures which do not contain pedestrians; cutting and extracting a detected human body bounding box (bbox) to generate a new picture image-c, and displaying the new picture image-c; and sequentially inputting the image-c into an OpenPose human skeleton extraction network, removing the influence of a complex background, complementingand screening different missing conditions of the skeleton diagram by using different methods, and finally outputting a complete skeleton diagram.

Description

technical field [0001] The invention is an OpenPose-based multi-view human skeleton automatic labeling method, which belongs to the technical field of unmanned driving. Background technique [0002] Safety is an important requirement for the transformation of driverless technology research results into products. In order to ensure the safety of autonomous driving, the vehicle needs to accurately perceive the surrounding vehicles, road information, traffic information and pedestrians. Autonomous driving vehicles should be able to recognize the actions of pedestrians, understand the intentions of pedestrians and make decisions, so that people and vehicles can interact well. [0003] At present, the data of pedestrian movement is collected from a single perspective. In a single perspective, when pedestrians block each other or pedestrians are blocked by other objects, it will have a certain impact on the accurate detection of pedestrians and pedestrian action recognition. If ...

AI Technical Summary

Problems solved by technology

However, there is no multi-view pedestrian action data set, so pedestrian action data collection under multi-view is carried out. Most of the existing public data sets are manually labeled, which takes a long time, high cost and non-standard labeling.

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