Nonlinear Motion Tracking by Deep Learning Architecture

3rd International Conference on Communication Systems

无线电电子学

Verma, Arnav^1 ; Samaiya, Devesh^2 ; Gupta, Karunesh K.^2

University of Edinburgh, United Kingdom^1

Birla Institute of Technology and Science, Pilani, India^2

关键词: Learning architectures;    Non-linear motions;    Object motion tracking;    Prior knowledge;    Real-time recurrent learning;    Single object;    Standard algorithms;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/331/1/012020/pdf
DOI  :  10.1088/1757-899X/331/1/012020

来源: IOP

【 摘 要 】

In the world of Artificial Intelligence, object motion tracking is one of the major problems. The extensive research is being carried out to track people in crowd. This paper presents a unique technique for nonlinear motion tracking in the absence of prior knowledge of nature of nonlinear path that the object being tracked may follow. We achieve this by first obtaining the centroid of the object and then using the centroid as the current example for a recurrent neural network trained using real-time recurrent learning. We have tweaked the standard algorithm slightly and have accumulated the gradient for few previous iterations instead of using just the current iteration as is the norm. We show that for a single object, such a recurrent neural network is highly capable of approximating the nonlinearity of its path.