Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (14): 163-167.

### Human action recognition based on dense trajectories with saliency detection

LU Tianran, YU Fengqin, YANG Huizhong, CHEN Ying

1. School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
• Online:2018-07-15 Published:2018-08-06

### 基于显著性检测和稠密轨迹的人体行为识别

1. 江南大学 物联网工程学院，江苏 无锡 214122

Abstract: Human action recognition based on dense trajectories samples the whole image of every frame densely, which leads to high feature dimensionality, large computational cost and containing the irrelevant background information. A human action recognition method is proposed based on dense trajectories with saliency detection. First, a multi-scale static saliency detection is used to get the action subject positions, which then is combined with the results of dynamic saliency detection to get human action areas. The original algorithm is improved by only extracting dense trajectories in these areas. To enhance adequacy of feature expression, Fisher vector is used to replace BOW model encoding the features. At last, SVM is used to get the results of human action recognition. The experimental results conducted on KTH dataset and UCF Sports dataset show that the proposed method has improved on the recognition accuracy compared with the original algorithm.