  Automated human action recognition is one of the most attractive and
practical research fields in computer vision, in spite of its high
computational costs. In such systems, the human action labelling is based on
the appearance and patterns of the motions in the video sequences; however, the
conventional methodologies and classic neural networks cannot use temporal
information for action recognition prediction in the upcoming frames in a video
sequence. On the other hand, the computational cost of the preprocessing stage
is high. In this paper, we address challenges of the preprocessing phase, by an
automated selection of representative frames among the input sequences.
Furthermore, we extract the key features of the representative frame rather
than the entire features. We propose a hybrid technique using background
subtraction and HOG, followed by application of a deep neural network and
skeletal modelling method. The combination of a CNN and the LSTM recursive
network is considered for feature selection and maintaining the previous
information, and finally, a Softmax-KNN classifier is used for labelling human
activities. We name our model as Feature Reduction & Deep Learning based action
recognition method, or FR-DL in short. To evaluate the proposed method, we use
the UCF dataset for the benchmarking which is widely-used among researchers in
action recognition research. The dataset includes 101 complicated activities in
the wild. Experimental results show a significant improvement in terms of
accuracy and speed in comparison with six state-of-the-art articles.
