Video target labeling method and device and electronic equipment

Abstract

The embodiment of the invention provides a video target labeling method and device and electronic equipment, and belongs to the technical field of data processing, and the method comprises the steps:obtaining a first labeling box formed for a first type of key frames in a target video; training a target detection module and a target tracking module for executing a labeling function based on the first type of key frames and the first labeling box after whether the number and quality of the first type of key frames meet preset requirements or not; the trained target detection module and the target tracking module are utilized to perform annotation operation on the remaining first type of key frames which are not annotated, and a second annotation box and a third annotation box are generated; after the second annotation box and the third annotation box are confirmed, marking the second annotation box and the third annotation box as a first annotation box again. Through the processing scheme of the invention, the labeling efficiency of the video target can be improved.

Description

technical field [0001] The present disclosure relates to the technical field of data processing, and in particular, to a video target labeling method, device and electronic equipment. Background technique [0002] Data labeling is a kind of labor-intensive work with high manual cost. It mainly relies on human knowledge to make some marks for visual data, which is academically called Ground Truth (true value). The artificial intelligence algorithm is trying to build a mathematical model, which can simulate human knowledge through learning, so that artificial intelligence can make judgments similar to human intelligence. [0003] Obtaining data annotation often requires a huge price. Every skilled data annotation engineer can only mark hundreds or even thousands of annotation boxes every day with simple tools. The data required for artificial intelligence training is indeed extremely huge. [0004] The quality of data annotation will also directly affect the accuracy of art...

AI Technical Summary

Problems solved by technology

When humans are engaged in such boring and mechanical work, fatigue is inevitable, which will seriously affect the quality of data labeling

[0005] There is a contradiction between the demand for massive data for artificial intelligence algorithm training and the low efficiency of human labeling, and there is a contradiction between mechanical and boring work content and high-quality requirements for work

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