Detection, Tracking, and Segmentation of Transient Construction Objects in Video Frames

In construction environments, transient construction objects such as moving vehicles, debris, materials, and construction tools can pose safety hazards and impede visibility. Monitoring their movement in video frames is crucial for effective project management, but it is challenging due to the complexity and dynamism of construction sites. This study proposes a two-step method to address this challenge by detecting, tracking, and segmenting transient construction objects in video frames. A synthetic dataset is used to train a deep learning-based detection model, and coarse and fine tracking models are then applied to track and segment objects based on detection results. The preliminary results reveal the significant performance of the proposed detection model, achieving an average precision of 75.75% at an IoU threshold of 0.5. It is demonstrated that the proposed detection method enables the detection of transient construction objects using synthetic datasets, reducing the need for manually annotating additional construction-related datasets. Besides, the produced segmentation results provide detailed information about the location and shape of objects, enabling enhanced safety control and analysis.