## 2 Literature Review
### Vision-Based Earthmoving Activity Detection  
Recently, deep learning techniques have been explored for detecting and continuously monitoring earthmoving activities of construction equipment, as well as assessing posture, cycle time, and estimating productivity of earthen material at construction sites. Various computer vision methods detect and recognize the activities of construction equipment by extracting features from consecutive frames of the visual stream from the cameras [24]. Various deep-learning algorithms have recently applied optical flow to track the motion of feature vectors and estimate the earthmoving activity of construction equipment. K-nearestneighbours (KNN), decision tree, and SVM have been utilized for human activity recognition using the Weizmann and UCF101 databases through an optical flow descriptor [26]. CNN-based backbone networks are fundamental in computer vision for detecting, tracking, and activity recognition of construction equipment [59]. A CNN architecture has been developed to automatically extract temporal gradient information before the activity decision based on the extracted features of raw RGB frames [60]. CNN methods use convolutional kernels to capture local data features and extract features from image and time series data by leveraging local connectivity, weight sharing, and pooling layers [10]. Using the hidden Markov model, a CNN network has been employed to detect and recognize sequential patterns in construction equipment activity [61]. The Multistage Temporal Convolutional Network (MS-TCN) has been utilized to enhance the performance of excavator activity recognition, synthesizing and analyzing 3D data [62]. RGB and optical flow data have been incorporated into a two-stream architecture for capturing spatial and temporal information in video action recognition [30]. An extended temporal gradient stream has been introduced to replace the optical flow stream, enhancing computing efficiency [63]. RGB, optical flow, and gray stream have been integrated for activity recognition and monitoring of construction workers using a deep stream CNN [17]. You Only Look Once (YOLO) has been used to identify idling reasons for excavators and dump trucks during the excavation operation [64]. The extended form YOLOv3 has been trained to recognize different activities, including Excavation, Concrete Screeding, Bricklaying, and Carpentry, for intelligent construction monitoring [65]. You Only Watch Once (YOWO) is another computer vision-based deep learning algorithm used for activity recognition and productivity monitoring of construction equipment by estimating the cycle time of the earthwork operation [20]. Recently, you only watch once (YOWO)-based modified Distillation of temporal Gradient data for construction Entity activity Recognition (DIGER) algorithm was presented to classify three activities of the excavator: digging, swinging, and loading the trucks [66]. However, this study utilized single-source external video data and considered only three activities. The sequential pattern in the spatiotemporal features of earthmoving activities of excavators has been exploited by various combinations of the CNN and LSTM models for excavator detection, tracking, and activity recognition through single-source external cameras [23]. Considering the computing capabilities of the model, the CNN-integrated Bidirectional LSTM (CNN-BiLSTM) algorithm has focused on directly classifying the single-action activity videos of the excavator (dumping, excavation, hauling, and swinging) [24]. Previous research has shown that CNN-LSTM effectively integrates CNNs for spatial feature extraction and LSTM networks for capturing temporal dependencies, making it highly suitable for analyzing sequential data in video-based activity recognition [67; 68]. CNN-LSTM remains computationally efficient and requires fewer resources, which makes it practical for real-time applications on edge devices and mobile platforms, particularly in construction sites [69].