Research Area:  Machine Learning

Abstract:

This paper proposes an efficient model named Light YOLO for hand gesture recognition on the embedded platforms. Light YOLO improves accuracy, speed, and model size, in three aspects. To deal with the small scale gestures in practical applications, we strengthen the YOLOv2 with a spatial refinement module to obtain fine-grained features. To accelerate the refined network, we propose a selective-dropout channel pruning approach to prune the redundancy convolution kernels in the network. Moreover, we introduce a dataset for hand gesture recognition in complex scenes. The experimental results on this dataset show that the proposed Light YOLO significantly improve the YOLOv2 network, i.e., accuracy from 96.80% to 98.06%, speed form 40PFS to 125FPS, and size form 250M to 4MB.

Keywords:  
Object detection
Gesture recognition
Model compression and acceleration
Machine learning

Author(s) Name:   Zihan Ni; Jia Chen; Nong Sang; Changxin Gao; Leyuan Liu

Publisher name:  IEEE

DOI:  10.1109/ICIP.2018.8451766

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/8451766