## 5 Conclusions  
There have been many studies carried out for the vision-based activity classification of construction equipment. Most of them use the traditional BOW and SVM approaches. However, the response of a pre-trained CNN model \"Googlenet\" and bidirectional long short-term memory (BiLSTM) had not yet been used for the activity recognition of construction equipment. The goal of this study was to practically demonstrate the application of Googlenet for feature extraction, and of BiLSTM for the training and execution of the classification of excavator activities. Additionally, the performance of integrated models of Googlenet and BiLSTM have to be tested for the activity recognition of excavators in the earthwork field. Therefore, the authors have implemented an integrated method based on a pre-trained convolution neural network (CNN) \"Googlenet\" and bidirectional long short-term memory (BiLSTM) for the activity classification of excavators. First, the pre-trained CNN model extracts the sequential pattern of visual features from the video frames of construction equipment and then feeds it into the BiLSTM. The BiLSTM consists of two LSTM layers, stacked on top of each other in both directions, with a forward direction as well as a backwards direction. The benefit of BiLSTM is that it not only includes the information from the previous frame but also from the upcoming frame. BiLSTM recognizes the different activities of the excavator after analyzing the input from the pre-trained convolution neural network. The bidirectional LSTM (BiLSTM) framework shows promising results on the YouTube-based excavator dataset. The experimental results show accuracies of 93.55% and 87.5% for the validation and test datasets, respectively. The experimental results of the CNN-BiLSTM framework show that the algorithm is capable of recognizing the different activities of construction equipment by using single-action videos datasets. The contribution to the knowledge of this research is the implementation and evaluation of the pre-trained CNN model \"Googlenet\" and bidirectional LSTM for the activity recognition of construction equipment. Furthermore, the authors intend to classify the value-added activities of the construction equipment in the future. There are some limitations in this research: (1) The activity recognition performance of the model is affected by the detection results in the presence of two or more excavators. (2) The light conditions of the construction site video during the operation impact on the activity recognition results. When the light was too bright or low during the construction operation, it is difficult to recognize the moving features in video frames. (3) The diversity of data sets used in this study is limited, which may have affected the activity classification performance. Considering the limitations and applications of the research, the future goals of this study are to (1) improve the robustness of the activity recognition under varying light conditions by using some filters and implementing multiple cameras for better visual results in moving frames. (2) Various data should be collected from construction sites under different light conditions with various activities of excavators being performed. Furthermore, the authors intend to classify the value-added activities of the construction equipment in the future. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Test Accuracy (\%)** & **Method** \\ \hline [PERSON] et al. [17] & 79 & 3D-Harris feature + Bayesian learning classifier \\ \hline [PERSON] et al. [31] & 57 & HOG, HOE, MBH features + SVM classifier \\ \hline [PERSON] et al. [10] & 86.33 & 3D HOG feature + SVM classifier \\ \hline [PERSON] et al. [32] & 80.5 & Faster R-CNN detector + Relevance network \\ \hline [PERSON] et al. [6] & 90.9 &
\begin{tabular}{c} Faster R-CNN detector + Tracking- \\ Learning-Detection tracker + CNN-LSTM \\ \end{tabular} \\ \hline Proposed framework & 87.5 & CNN(GoogleNet) and BLSTM \\ \hline \hline \end{tabular}
\end{table}