# Vessel Velocity Estimation and Docking Analysis: A Computer Vision Approach
## 3 Related Works  
[PERSON] et al. (2015) proposed a system that uses two cameras to estimate the velocity of ships. In addition, the proposed system detects and tracks ships through re-identification (re-ID) tasks. The re-ID task is used to link ships seen by the two cameras. The authors introduced a new Vessel-reID dataset to train and evaluate the model. In this dataset, the authors evaluated various machine learning models. They found that the SSD multi-box detector model can cover a large portion of the total trajectory to re-identify vessels through successful detections. The authors' system consists of two stages: camera processing and vessel re-identification. The camera processing stage detects and tracks vessels in real time, storing crops and a timestamp in the database to create a unique feature vector description. The second stage evaluates visually matching vessels using travel time constraints and generates an alert if a match is found for a vessel that traveled faster than the speed limit. The authors highlight the challenges identified, indicating that measuring the velocity of a vessel using only one camera is affected by wind interference, causing small camera movements, difficulty in establishing the exact position of the vessel, and weather and lighting conditions. Hence, measuring during larger displacements is recommended. In addition, different types of vessels are also a challenge, including those that occupy the entire camera image or small rowing vessels that occupy small pixels in the image. [PERSON] et al. (2016) presented a method for detecting and tracking ships. The method consists of a Moving Vessel Detection Algorithm (MVDA), Status Update Algorithm (SUA), and Temporary Buffer (TB). The MVDA returns the probable bounding boxes of ships in the scene given a video input. The results are stored in the TB at the end of each prediction round, and the SUA analyzes the contents of the TB, filters probable artifacts, and returns zero or more images of moving ships. In addition to the vessel detection and tracking algorithms, the authors applied a water detection algorithm to help the model discard water features behind ships. The water detection algorithm used the contours of objects, and for each contour, it calculated the length and area occupied. If the length or area of the contour was smaller than a previously established threshold, that region was labeled \"water\". This additional step was important because water can often be mistaken for a ship's hull, leading to false positives and inaccurate tracking. This is particularly important in scenarios where multiple vessels are present, and there is a lot of movement in the water, such as in busy ports or shipping lanes. The authors, like [PERSON] et al. (2015), have identified that incorrect detections frequently occur due to unfavorable lighting conditions. In addition, since some evaluated scenarios come from cameras with less color saturation, the background subtraction algorithm performs worse in these scenarios. Regarding the docking problem, [PERSON] et al. (2016) presented a new computer vision technique for monitoring docked ships. The proposed technique uses visual feature correlation of ship images to estimate its movement over time. The similarity metric used, Pearson similarity, according to the authors, produced a robust model to handle changes in illumination, allowing the model to perform well in various scenarios. During the tests, this approach had promising results. The heave motion recorded with the optical method showed a very satisfactory correspondence with the wave height measured by Radar. [PERSON] and [PERSON] (2017) utilized the structural similarity (SSIM) measure ([PERSON] and [PERSON], 2017) for detecting anomalies. SSIM is a widely used image quality assessment method that compares the structural similarity between two images, considering luminance, contrast, and structural information. This method has been shown to perform well in detecting changes in complex scenes, such as traffic videos.