Dynamic vision sensor sample set modeling method based on frame images

Abstract

The invention provides a dynamic vision sensor sample set modeling method based on a frame image, which can realize quick extraction of a moving target and has the advantages of low delay, low storagespace, high dynamic range and the like. The method comprises the following steps: S1, acquiring to-be-processed video data; s2, performing pixel normalization processing and pixel light intensity accumulation processing on continuous frame images in the to-be-processed video data; s3, performing unit discretization on the processed frame image to obtain a single pixel unit; s4, for each single pixel unit, performing calculating according to the light intensity change amplitudes of the current frame and the adjacent frame, and judging whether a trigger threshold T is reached or not; s5, storing the data information corresponding to the single pixel unit output in the step S4 according to a self-defined storage format, wherein the self-defined storage format comprises address event data corresponding to a single pixel unit; s6, coding all the stored data information according to actual requirements to form final sample set data.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a method for modeling a dynamic visual sensor sample set based on frame images. Background technique [0002] In the field of image recognition, with the development of deep learning, convolutional neural network has become an important research content in the field of computer vision, and has excellent performance in image classification, target recognition and intelligent monitoring. However, the existing CNN-based target recognition frameworks rely on huge data sets and complex training processes. Moreover, these methods all use ordinary images as samples for calculation and processing, which contain a lot of redundant information, occupy a large storage space, and also face problems such as difficulty in separating noise and effective information. These problems make the image recognition process require high hardware configuration and power consumption to meet the...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that the existing technology based on convolutional neural network target recognition samples has a large amount of redundant information, which leads to the problem that the target cannot be quickly identified, the present invention provides a dynamic visual sensor sample set modeling method based on frame images, which can realize Fast extraction of moving targets, and has the advantages of low latency, low storage space and high dynamic range

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