# Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning
## 4 Case Study and Results Analysis  
Due to the lack of relevant databases for training and testing visual activities of scaffolding construction operations, before validating our approach it is crucial to collect a corresponding video sequence dataset. As shown in Figures 9 and 10, the target scaffolding operation comprises 3 activities: scaffold erecting, transporting and idling/waiting. Cameras with 12 megapixels were used to capture video data in a real construction environment in Western Australia, as well as in the laboratory of the Australasian Joint Research Centre for Building Information Modelling in Curtin University. Professional volunteers were recruited to conduct the same activities both under the actual and laboratory construction condition. Workface assessment requires a supervisor to clearly observe and record entire scaffolding activities from a certain distance. This observing distance ranges from 5 m to 20 m, which allows the supervisor to identify distinct activities. Thus, in our approach, portable video cameras were placed 5 m to 20 m away from the scaffolding activities. To increase the variety of the database, the camera shot angle was set at three height levels: the first one was located between ground level and kneel level, the second one was located between kneel level and eye level and the third one was located over the head level. Horizontal camera angles ranged from a frontal angle, a three-quarter front angle to a profile angle (a view from the side). Some 52 clips of video regarding relevant scaffolding activities were collected and some of these videos are trimmed, in that they last more than 8 s and only include one single activity for training and testing purposes, and the rest of the videos covering several scaffolding activities are stored for validation. Some 40 clips were collected in the lab and the remaining 12 clips were collected on-site. The duration of the clips lasts from 3 to 10 min. The captured video has 25 fps and 1080p resolution. Every video clip is processed into a stack of image frames and the whole dataset consists of 1731 frames. Additionally, these frames are annotated with their corresponding activity category including erecting, transporting and idling/waiting as the input for the purpose of supervised learning. Every video clip that merely includes one category of scaffolding activity was processed into image frames. For each image frame, its corresponding activity category was added as one additional feature among 42 features of body key points. To evaluate the performance of our proposed approach for construction activity analysis, a case study was carried out. The methodology for activity classification was achieved in a Python-based environment and open-source package Scikit-learn with the assistance of a DELL workstation with a 1.9 GHz processing unit and 32 GB RAM. As it is shown in Table 2, the mean accuracy of selected supervised classifiers under 10-folds cross-validation regarding our scaffolding dataset was 96.58% (RF), 94.24% (SVM), 92.08% (DT), 96.13% (KNN), 93.12% (NN) respectively. All the classifiers' general accu  
Figure 10: Video sequences of scaffolding activity: (**a**) scaffolding effecting; (**b**) transporting; (**c**) waiting or idling. Figure 9: Experiment settings: the camera shot angle was set at three height levels: kneel level, eye level and overhead level and the horizontal camera angles range from the frontal angle, and three-quarter front angle to profile angle. racy achieved over 90%, while RF and KNN outperformed the rest with an accuracy of around 96%. Since 3D pose classification is a transitional step in our model, every video frame of scaffolding work can be classified as one of three scaffolding activities. The results of 3D pose estimations include the classification results for every video frame as well as the visualization of 3D key joints and pose estimation.