Comparing to other sectors, the construction sector suffers from low productivity, and it is not improving over time due to the unique nature of construction projects. It is believed that construction productivity cannot be improved without efficient monitoring and measuring, and this is crucial for project success. There are many limitations for the traditional construction productivity monitoring practices like time and cost consuming and error-prone. Although a lot of studies have been implemented to eliminate these limitations, a gap still exists in the automated monitoring of construction productivity. This study proposes an automated monitoring model for indoor productivity recognition in construction projects. The model will provide an instant evaluation of the project productivity which will enhance the optimum utilization of the project resources. The proposed model will be developed by first generating a baseline for the activities state which will be represented as baseline state model. Then the as-built model will be generated. Preliminary experimentation was performed on selected images where the number of tiles and bricks was obtained. The experimentation was performed using Open-Source Computer Vision Library (OpenCV). Preliminary results depict that by using the proposed model the automated monitoring of productivity is achievable. Although, there is a need of dedicated efforts for improvement and development of technique for more effective and efficient results. © 2021 IEEE.