Target recognition system and method based on time slice convolutional neural network

Abstract

The invention relates to a target recognition system and method based on a time slice convolutional neural network. The system comprises an event stream segmentation module used for segmenting an event stream sample to form an event set, representing the event set into pseudo images through an event representation method, splicing the pseudo images into a first feature map according to time slice convolutional neural network input channels, and then assigning different weights to each channel again to obtain a second feature map; a feature extraction module which is used for inputting the second feature map into a time slice convolutional neural network for feature extraction to obtain a feature map of a preset specification; and a classification module which is used for converting the feature map of the preset specification into a vector to obtain a target recognition result. According to the method, the event stream is segmented by using the time correlation of the event stream, and meanwhile, the method is fused with an original deep learning method and is effectively compatible with all event representation methods for target recognition, so that the recognition efficiency and precision are improved.

Description

technical field [0001] The present application relates to the technical field of target recognition, and more specifically, the present application relates to a target recognition system and method based on a time-sliced ​​convolutional neural network. Background technique [0002] Event camera is a new type of neuromorphic vision sensor, also known as dynamic vision sensor. It captures dynamic changes in the scene based on an event-driven approach and responds to pixel-level brightness changes. It has the advantages of low latency, low power consumption, high dynamic range, and high time resolution. It has attracted more and more attention from people inside and outside the industry. support. With the development of convolutional neural networks, computer vision methods have made great progress. However, due to the sparsity and asynchrony of event streams, traditional computer vision algorithms cannot be directly applied, and existing methods are mainly divided into metho...

AI Technical Summary

Problems solved by technology

The challenge of this problem is how to integrate with the original deep learning method and be effectively compatible with all event representation methods for target recognition while utilizing the temporal correlation of event streams.

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