  In this paper the research on optimisation of visual object tracking using a
Siamese neural network for embedded vision systems is presented. It was assumed
that the solution shall operate in real-time, preferably for a high resolution
video stream, with the lowest possible energy consumption. To meet these
requirements, techniques such as the reduction of computational precision and
pruning were considered. Brevitas, a tool dedicated for optimisation and
quantisation of neural networks for FPGA implementation, was used. A number of
training scenarios were tested with varying levels of optimisations - from
integer uniform quantisation with 16 bits to ternary and binary networks. Next,
the influence of these optimisations on the tracking performance was evaluated.
It was possible to reduce the size of the convolutional filters up to 10 times
in relation to the original network. The obtained results indicate that using
quantisation can significantly reduce the memory and computational complexity
of the proposed network while still enabling precise tracking, thus allow to
use it in embedded vision systems. Moreover, quantisation of weights positively
affects the network training by decreasing overfitting.
