Motion-feature-based activity recognition methods extract the features from the consecutive frames of the video and convert the spatial and temporal information of the features into feature vectors. A computer-vision-based algorithm, Support vector machine (SVM), was presented to classify the single action of the excavator using spatiotemporal visual features [10]. The average accuracy of action recognition for the excavator and dump truck was 86.33% and 98.33%, respectively. Similar work was presented by 3D-Haris and local histograms to extract the features, and a Bayesian network was used to classify the excavator activities (relocating, excavating, swinging) instead of a Support vector machine (SVM) [17]. Computer vision and modern pattern recognition algorithms have outperformed the traditional approaches (support vector machines, linear regression, and Bayesian networks) in the field of image classification [25], Object detection [26], and action recognition [5; 18]. The complex relationship of input and output classes of dynamic processes is solvable with sophisticated neural networks such as a CNN and recurrent neural network (RNN). CNN-based methods were found to be the best method for detecting the knife among the Bag of Words (BOWs), Hog-SVMs, pre-trained Alexnet and SVMs, and CNNs for safety purposes [26]. It was also found that Alexnet with an SVM provides the best accuracy and the time for training the algorithm was much higher for the CNN; however, the predicted time is higher for Alexnet than the CNN. In another study, a pre-trained CNN network VGG-16 was used to integrate the RGB, optical flow, and grey stream input data collected from 12 different construction sites in Wuhan city to automatically identify the activities (walking, transporting, and steel bending) of the worker accurately, with a precision of 91%, 92%, and 100%, respectively [18]. For the fine-tuning of the RGB input data in this study, the dropout ratio was set at 0.9 to avoid overfitting. The overall accuracy and recall were 85% and 100%, respectively, to detect and classify the worker's activity. Furthermore, a framework (R-CNN, SORT, 3D ResNet) was presented to recognize the activities automatically (digging, loading, and swinging) and estimate the productivity of the excavator [11]. The average accuracy of activity recognition for the input data collected from the 21 construction sites was 87.6%, and the precision and recall for the digging, loading and swinging was 95% and 86%, 86% and 93%, and 84% and 80%, respectively. The accuracy of the productivity calculation was 83%. CNN has been used widely for the detection of objects and the classification of activities based on this literature. Pre-trained CNNs have also been reported for feature extraction, such as Alexnet, VGG-16, and 3D ResNet. However, the application of Googlenet has not been reported yet in the field of the construction industry, providing a potential application of Googlenet for the extraction of features. Recurrent neural networks (RNNs) are well considered for time series classification problems, especially when assisted with convolutional neural networks (CNNs)/pre-trained CNNs. Spatial correlations of visual features extracted by CNNs are provided to the RNN models, and, hence, an integrated system of CNN and RNN can provide better performance. LSTM is one of the RNN models and has been reportedly used for time series classification problems. A comparison of the behavioral analysis between the LSTM and BiLSTM was conducted to evaluate their structural differences, effect on accuracy, and time required to reach equilibrium [27]. It was noted that the accuracy of the BiLSTM has increased and reduced errors by 37.78%. However, the time required to reach equilibrium was more for the BiLSTM. Furthermore, various studies have investigated the effect of BiLSTM in other fields. A CNN- and Recurrent neural network (RNN)-based algorithm was presented to classify the emotion of the Word2 vec database [28].