The ECI4.0 project, known as "Intelligent Commercial Spaces," was initiated with the goal of developing and validating a multimodal platform for intelligent analysis of human behavior patterns in commercial areas. This platform was designed to leverage advanced technologies such as computer vision, sensor fusion, and machine learning techniques. The primary aim was to enhance customer experience in retail spaces, located in large shopping centers or airports, by identifying customer profiles and behaviors through these innovative technologies.

As the project progressed, a significant challenge was encountered. While the initial objective included the identification of specific characteristics of individuals (such as age group, gender, or relationship with other customers), this aspect could not be achieved in the final implementation. The reason for this limitation was the project's commitment to privacy and ethical standards, leading to the use of anonymized images, obscuring or removing any identifiable features of individuals in the surveillance footage to protect their privacy. 

Despite this limitation, the project was successful in other areas. An occlusion-aware mechanism was introduced to manage irregularities in the pedestrian trajectory data and identify the parts of the human body that were occluded, using skeleton data generated by human pose estimation algorithms. It effectively analyzed the movement patterns of customers within the commercial spaces. This analysis included tracking the paths and routes taken by customers, which provided valuable insights into how individuals navigate stores, their interactions with different sections, and the general flow of traffic.