FPGA Based Real-Time Multi-Face Detection System With Convolution Neural Network
Huajie Xu, Zhaohui Wu, Jie Ding, Bin Li, Lanbo Lin, Jia Song Zhu, Zhijie Hao · 2019
The AdaBoost-based real-time face detections have been widely used in current video surveillance. However, the AdaBoost-based face detection has poor performances in detecting multi-face with different scales, multiple poses, and occlusion in complex lighting environment. Recent research shows that the convolutional neural network (CNN) can improve its accuracy. In this work, a FPGA based real-time multi-face detection system for crowded area surveillance application using CNN is presented. A hardware friendly fully quantization strategy is proposed and the result is tested on WIDER FACE dataset. With acceptable loss of accuracy, the FPGA based system can achieve a frame rate of 37 FPS at $512 \times 288$ resolution with only 65 ms processing delay.