  With the rapid increase in digital technologies, most fields of study include
recognition of human activity and intention recognition, which are essential in
smart environments. In this study, we equipped the activity recognition system
with the ability to recognize intentions by affecting the pace of movement of
individuals in the representation of images. Using this technology in various
environments such as elevators and automatic doors will lead to identifying
those who intend to pass the automatic door from those who are passing by. This
system, if applied in elevators and automatic doors, will save energy and
increase efficiency. For this study, data preparation is applied to combine the
spatial and temporal features with the help of digital image processing
principles. Nevertheless, unlike previous studies, only one AlexNet neural
network is used instead of two-stream convolutional neural networks. Our
embedded system was implemented with an accuracy of 98.78% on our intention
recognition dataset. We also examined our data representation approach on other
datasets, including HMDB-51, KTH, and Weizmann, and obtained accuracy of
78.48%, 97.95%, and 100%, respectively. The image recognition and neural
network models were simulated and implemented using Xilinx simulators for the
Xilinx ZCU102 board. The operating frequency of this embedded system is 333
MHz, and it works in real-time with 120 frames per second (fps).
