To help machines learn what we human beings are doing via a camera is important. Once it comes true, machines can make different responses to all kinds of human's postures. But the process is very difficult as well, because usually it is very slow and power-consuming, and requires a very large memory space. Here we focus on real-time posture recognition, and try to make the machine "know" what posture we make. The posture recognition system is consisted of DE10-Nano SoC FPGA Kit, a camera, and an HDMI monitor. SoC FPGA captures video streams from the camera, recognizes human postures with a CNN model, and finally shows the original video and classification result (standing, walking, waving, etc.) via HDMI interface.
We upload our thesis here. And the details of the project is demonstrated.
We upload our projects, including Matlab, Python, and Quartus.
And the software version is:
Matlab r2017b
Python 3.6.3
Anaconda 5.1.0
TensorFlow-gpu 1.3.0
Quartus 14.0