# Vessel Velocity Estimation and Docking Analysis: A Computer Vision Approach
###### Abstract  
The opportunities for leveraging technology to enhance the efficiency of vessel port activities are vast. Applying video analytics to model and optimize certain processes offers a remarkable way to improve overall operations. Within the realm of vessel port activities, two crucial processes are vessel approximation and the docking process. This work specifically focuses on developing a vessel velocity estimation model and a docking mooring analytical system using a computer vision approach. The study introduces algorithms for speed estimation and mooring bitt detection, leveraging techniques such as the Structural Similarity Index (SSIM) for precise image comparison. The obtained results highlight the effectiveness of the proposed algorithms, demonstrating satisfactory speed estimation capabilities and successful identification of tied cables on the mooring bits. These advancements pave the way for enhanced safety and efficiency in vessel docking procedures. However, further research and improvements are necessary to address challenges related to occlusions and illumination variations and explore additional techniques to enhance the models' performance and applicability in real-world scenarios. vessel velocity estimation; docking analysis; computer vision; Structural Similarity Index (SSIM) +
Footnote †: journal: _algoritelms_  
002331320233343  
In this context, the anchoring process can be modeled through video analytics. Every ship docking structure in a port has a capacity limit to absorb the mechanical energy transported by the docking vessel. An important factor during the docking of vessels is the approach velocity. Designing an analytics tool that measures docking velocity is crucial for the port, as approach speeds above the limit can cause damage to the port's infrastructure [3]. In addition, ship behavior prediction is critical for early detection of potentially risky behavior, such as ship collision. Such a model could also improve maritime traffic efficiency [4]. Currently, in SUAPE, such information is not available at all docking berths, and when available, it comes from more expensive third-party technologies, such as lasers. Docking berths have structures called mooring bollards. The mooring bollards are structures where cables that hold the ships are tied. The mooring of these cables is used to determine the exact time of docking and undocking of the ships. Therefore, identifying the exact moment of docking of the ships is of utmost importance for port administration to know the vessel's time of stay. Currently, in SUAPE, port management relies on the labor of employees to identify the docking and undocking. It is important to note that in several ports, the criteria of docking and undocking are established based on subjective observations of operators and shipmasters, without particularized studies to support them [5]. Designing video analytics for these two problems has an impact on both infrastructure and revenue. From an infrastructure perspective, the Port Authority's interest in preserving the equipment involved in mooring processes becomes relevant. From a revenue perspective, it begins to be calculated when the first cable is tied to the bollard during mooring and ends when the last cable is removed during unmooring. In addition, the information from this monitoring will provide the Port Authority with subsidies for determining and assigning responsibilities in the case of incidents and damage to the equipment involved in the mooring process. This work aims to utilize two computer vision techniques to address these problems. The border-following algorithm is employed in the velocity estimation model, while the Structural Similarity Index Measure (SSIM) is utilized to estimate the time of vessel docking and undocking. All experiments presented in this paper were conducted using a dataset provided by the port authorities of SUAPE.