The leaky integrate-and-fire neuron model for a rigid and a non-rigid object tracking

被引：0

作者：

Yedjour, Hayat ^{[1
]}

Meftah, Boudjelal ^{[2
]}

Yedj, Dounia ^{[1
]}

Benyettou, Abdelkader ^{[1
]}

机构：

[1] Univ Mohamed Boudiaf, Lab Signal & Parole, Oran, Algeria

[2] Univ Mustapha Stambouli, Lab LRSBG, Mascara, Algeria

来源：

PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND NEW TECHNOLOGIES (ICSENT '18) | 2018年

关键词：

Spiking neural network; leaky integrate and fire neuron model; edge detection; object tracking; Hausdorff distance;

D O I：

10.1145/3330089.3330096

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Spiking neural networks (SNNs) fall into the third generation of artificial neural network models, increasing the level of realism in a neural simulation. In this paper, a spiking neural network is presented for detecting and tracking of a moving object in video sequences with a static camera. The motion estimation of the object is carried out by minimizing a Hausdorff distance measure. The system has been successfully tested with various real video sequences. The results showed that our system can track the identified target over subsequent video frames even in occlusion case.

引用

页数：4

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