Spatio-Temporal Deep Feature Fusion for Human Action Recognition

Abstract

Action Recognition plays a vital role in many secure applications. The objective of this paper is to identify actions more accurately. This paper focuses on the two stream network in which keyframe extraction method is utilized before extracting spatial features. The temporal features are extracted using Attentive Correlated Temporal Feature (ACTF) which uses Long Short Term Memory (LSTM) for deep features. The spatial and temporal features are fused and classified using multi Support Vector Machine (multiSVM) classifier. Experiments are done on HMDB51 and UCF101 datasets. The results of the proposed method are compared with recent methods in terms of accuracy. The proposed method is proved to work better than other methods by achieving an accuracy of 96% for HMDB51 dataset and 98% for UCF101 dataset.