Event camera target recognition method based on self-attention mechanism

Abstract

The invention discloses an event camera target recognition method based on a self-attention mechanism. The event camera target recognition method comprises the following steps: S1, initializing an event camera; s2, completing a data acquisition task by using the initialized event camera; s3, performing image conversion on the collected event camera data, so that the event camera data can be used for a target identification task; s4, performing feature extraction on the event camera data after image conversion by using the trained network to obtain depth features of the target; s5, inputting the extracted depth features into a self-attention mechanism model for self-attention calculation to obtain target types included in the event camera data at the current moment; and S6, performing result output on the features subjected to self-attention calculation, and taking a result with the highest confidence coefficient as a final result to be output. The method solves the technical problems that current event camera training data is insufficient, and a traditional deep learning method is weak in global perception capability of event camera data.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a method for recognizing an event camera target based on a self-attention mechanism. Background technique [0002] Target recognition technology has been widely used in various fields of our daily life. Traditional target recognition is based on traditional RGB cameras. In the past few decades, deep learning technology has gradually become popular and has become a popular recognition technology. However, with the continuous application of technology, the recognition scheme based on traditional RGB cameras has certain defects. For example, traditional RGB cameras use a series of frame pictures to obtain external world data, and there is not only a large amount of information redundancy between consecutive frames. , and due to the relatively fixed refresh rate of frame acquisition events, it is easy to lose some key information between adjacent frames. At the same...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method for event camera target recognition based on a self-attention mechanism, which solves technical problems such as insufficient training data of current event cameras and weak global perception of event camera data by traditional deep learning methods

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