Huawei Technologies Co., Limited

Zhejiang University

Zhejiang Lab

Huajin Tang

Gang Pan

Jie Cheng

Lei Jiang

Xiao Wang

Dong Xing

Wenyi Zhao

Mengxiao Zhang

Chaofei Hong

Qianhui Liu

Haibo Ruan

Mengwen Yuan

De Ma

Yujing Lu

A target tracking method and a target tracking system of a spiking neural network based on an event camera are provided. The method includes: acquiring a data stream of asynchronous events in a high dynamic scene of a target by an event camera as input data; dividing the data stream of the asynchronous events into synchronous event frames with millisecond time resolution; training a twin network based on a spiking neural network by a gradient substitution algorithm with a target image as a template image and a complete image as a searched image; and tracking the target by a trained twin network with interpolating a result of feature mapping to up-sample and obtaining the position of the target in an original image. The twin network includes a feature extractor and a cross-correlation calculator.

Target tracking method and system of spiking neural network based on event camera

In an object recognition method, an object recognition device obtains AER data of a to-be-recognized object, wherein the AER data includes a plurality of AER events of the to-be-recognized object, each AER event comprising a timestamp and address information. The object recognition device extracts a plurality of feature maps of the AER data. Each feature map including partial spatial information and partial temporal information of the to-be-recognized object, and the partial spatial information and the partial temporal information are obtained based on the timestamp and the address information of each AER event. The object recognition device then recognizes the to-be-recognized object based on the plurality of feature maps of the AER data.

Object recognition method and apparatus

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Huajin Tang