FPGA-based Implementation of Hand Gesture Recognition Using Convolutional Neural Network

Abstract

Convolutional Neural Network (CNN) is a deep learning algorithm which is widely used in image processing and pattern recognition due to its robustness to feature invariance. However, it is also computation-intensive that results in a bad real-time performance. Field Programmable Gate Arrays (FPGA) has good performance in energy-efficiency, flexibility of parallel processing and pipelined operations. Thus it is expected to be used for accelerating deep learning algorithm. In this research, a FPGA based system is developed to realize the real-time Hand Gesture Recognition. We train a designed CNN model with caffe framework and obtain the model's parameters on PC. Bilinear interpolation algorithm is used to adjust the size of the image captured by camera. Then we use FPGA to implement the inference process of Hand Gesture Recognition with obtained parameters by designing an accelerator using Xilinx SDx tools.