  The ability to attend to salient regions of a visual scene is an innate and
necessary preprocessing step for both biological and engineered systems
performing high-level visual tasks (e.g. object detection, tracking, and
classification). Computational efficiency, in regard to processing bandwidth
and speed, is improved by only devoting computational resources to salient
regions of the visual stimuli. In this paper, we first present a neuromorphic,
bottom-up, dynamic visual saliency model based on the notion of proto-objects.
This is achieved by incorporating the temporal characteristics of the visual
stimulus into the model, similarly to the manner in which early stages of the
human visual system extracts temporal information. This neuromorphic model
outperforms state-of-the-art dynamic visual saliency models in predicting human
eye fixations on a commonly used video dataset with associated eye tracking
data. Secondly, for this model to have practical applications, it must be
capable of performing its computations in real-time under low-power,
small-size, and lightweight constraints. To address this, we introduce a
Field-Programmable Gate Array implementation of the model on an Opal Kelly 7350
Kintex-7 board. This novel hardware implementation allows for processing of up
to 23.35 frames per second running on a 100 MHz clock - better than 26x speedup
from the software implementation.
