  Siamese trackers have been among the state-of-the-art solutions in each
Visual Object Tracking (VOT) challenge over the past few years. However, with
great accuracy comes great computational complexity: to achieve real-time
processing, these trackers have to be massively parallelised and are usually
run on high-end GPUs. Easy to implement, this approach is energy consuming, and
thus cannot be used in many low-power applications. To overcome this, one can
use energy-efficient embedded devices, such as heterogeneous platforms joining
the ARM processor system with programmable logic (FPGA). In this work, we
propose a hardware-software implementation of the well-known fully connected
Siamese tracker (SiamFC). We have developed a quantised Siamese network for the
FINN accelerator, using algorithm-accelerator co-design, and performed design
space exploration to achieve the best efficiency-to-energy ratio (determined by
FPS and used resources). For our network, running in the programmable logic
part of the Zynq UltraScale+ MPSoC ZCU104, we achieved the processing of almost
50 frames-per-second with tracker accuracy on par with its floating point
counterpart, as well as the original SiamFC network. The complete tracking
system, implemented in ARM with the network accelerated on FPGA, achieves up to
17 fps. These results bring us towards bridging the gap between the highly
accurate but energy-demanding algorithms and energy-efficient solutions ready
to be used in low-power, edge systems.
