Gesture trajectory recognition for embedded platform
Aiming at the fact that most embedded platforms have weak computing power and cannot run neural network-based application (App)in real-time,a gesture interaction method based on Anchor-Free lightweight convolutional neural network(CNN)is proposed.This method alleviates the drawbacks of Anchor-Based detection,detects human hands in real-time through neural network,and uses dynamic time warping(DTW)algorithm to classify gesture trajectories.The quantity of neural network parameters is only 0.22 M.On the self-built hand detection dataset,the mean average precision(mAP)intersection over union(IoU)=0.50:0.95 can reach 68％.On the RK3568 embedded platform,for inference and post-processing per frame is only 31 ms,time comsuming of trajectory classification is only 43 ms,and CPU usage rate is only 34％,which meets real-time requirements.
lightweight convolutional neural networkAnchor-Freetarget detectiongesture classificationembedded system
NETL
NSTL
万方数据
