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.
Embedded vision device integration via OPC UA: Design and evaluation of a neural network-based monitoring system for Industry 4.0
