Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains a variety of keypoints and combines the multi-level strategy with an optical flow algorithm to search the keypoints with a large motion vector for tracking. Then, an outlier removal method based on Hamming distance and interquartile range (IQR) score is introduced to minimize the error. The proposed target tracking method is verified through a lab experiment—a three-story shear building structure subjected to various harmonic excitations. It is compared with existing sparse-optical-flow-based target tracking methods and target tracking methods based on three other types of techniques, i.e., feature matching, dense optical flow, and template matching. The results show that the performance of target tracking is greatly improved through the use of a multi-level strategy and the proposed outlier removal method. The proposed sparse-optical-flow-based target tracking method achieves the best accuracy compared to other existing target tracking methods.

/pdf/computer-vision-based-vibration-tracking-using-a-digital-6fjpmtfg.pdf

Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

Ship emissions can form linear cloud structures, or ship tracks, when atmospheric water vapor condenses on aerosols in the ship exhaust. These structures are of interest because they are observable and traceable examples of MCB, a mechanism that has been studied as a potential approach for solar climate intervention. Ship tracks can be observed throughout the diurnal cycle via space-borne assets like the advanced baseline imagers on the national oceanic and atmospheric administration geostationary operational environmental satellites, the GOES-R series. Due to complex atmospheric dynamics, it can be difficult to track these aerosol perturbations over space and time to precisely characterize how long a single emission source can significantly contribute to indirect radiative forcing. We propose an optical flow approach to estimate the trajectories of ship-emitted aerosols after they begin mixing with low boundary layer clouds using GOES-17 satellite imagery. Most optical flow estimation methods have only been used to estimate large scale atmospheric motion. We demonstrate the ability of our approach to precisely isolate the movement of ship tracks in low-lying clouds from the movement of large swaths of high clouds that often dominate the scene. This efficient approach shows that ship tracks persist as visible, linear features beyond 9 h and sometimes longer than 24 h.

An Optical Flow Approach to Tracking Ship Track Behavior Using GOES-R Satellite Imagery

Autonomous and unmanned shipping are currently trending research topics within the maritime sector, with the promise of a reduction in operating costs and an increase in safety. Although they bring higher investment costs, due to the long lifetime of ships, autonomous ships are expected to bring savings during ship exploitation. This paper aims to analyze capital and operating costs of five different sizes and route length container ships (conventional ships), and under a set of assumptions analyze the same costs for equivalent autonomous ships. A ship cost model is formed, where the typical cost scheme (investment and exploitation costs) is extended by the potential carbon pricing. Carbon pricing is taken into account due to the fact that the design procedure for autonomous and unmanned ships requires the employment of a next-generation regulatory framework. All results indicate the significant economic benefit of autonomous ships over conventional ones. Sensitivity analysis reveals that fuel and emission costs have a great influence on the overall profitability of autonomous vehicles. Although the literature review indicates that reduced operating costs due to crew removal will bring savings for autonomous shipping, results show that savings due reduced operating costs is minor.

/pdf/investigation-of-the-viability-of-unmanned-autonomous-1vb6341u.pdf

Investigation of the Viability of Unmanned Autonomous Container Ships under Different Carbon Pricing Scenarios

Ship emissions can form linear cloud structures, or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ship tracks</i> , when atmospheric water vapor condenses on aerosols in the ship exhaust. These structures are of interest because they are observable and traceable examples of MCB, a mechanism that has been studied as a potential approach for solar climate intervention. Ship tracks can be observed throughout the diurnal cycle via space-borne assets like the advanced baseline imagers on the national oceanic and atmospheric administration geostationary operational environmental satellites, the GOES-R series. Due to complex atmospheric dynamics, it can be difficult to track these aerosol perturbations over space and time to precisely characterize how long a single emission source can significantly contribute to indirect radiative forcing. We propose an optical flow approach to estimate the trajectories of ship-emitted aerosols after they begin mixing with low boundary layer clouds using GOES-17 satellite imagery. Most optical flow estimation methods have only been used to estimate large scale atmospheric motion. We demonstrate the ability of our approach to precisely isolate the movement of ship tracks in low-lying clouds from the movement of large swaths of high clouds that often dominate the scene. This efficient approach shows that ship tracks persist as visible, linear features beyond 9 h and sometimes longer than 24 h. 

The regional monitoring systems aim to recognize and localize the target of interest in the region area. However, the target detection algorithm currently used in the regional monitoring system has problems such as low recognition probability under complex background conditions. The type of moving object recognized by the background modelling algorithm is difficult to judge. This paper proposes a monitoring area target detection method that fuses the detection results of the two YOLOv5 target detection algorithms and the Vibe background modelling method through Kalman filtering. Experiments show that the proposed method can improve the consistency and stability of target detection results in regional monitoring scenarios. 

Research on Target Detection of Regional Monitoring with Complex Background using CNN and Background Modelling

Moving object detection and tracking are technologies applied to wide research fields including traffic monitoring and recognition of workers in surrounding heavy equipment environments. However, the conventional moving object detection methods have faced many problems such as much computing time, image noises, and disappearance of targets due to obstacles. In this paper, we introduce a new moving object detection and tracking algorithm based on the sparse optical flow for reducing computing time, removing noises and estimating the target efficiently. The developed algorithm maintains a variety of corner features with refreshed corner features, and the moving window detector is proposed to determine the feature points for tracking, based on the location history of the points. The performance of detecting moving objects is greatly improved through the moving window detector and the continuous target estimation. The memory-based estimator provides the capability to recall the location of corner features for a period of time, and it has an effect of tracking targets obscured by obstacles. The suggested approach was applied to real environments including various illumination (indoor and outdoor) conditions, a number of moving objects and obstacles, and the performance was evaluated on an embedded board (Raspberry pi4). The experimental results show that the proposed method maintains a high FPS (frame per seconds) and improves the accuracy performance, compared with the conventional optical flow methods and vision approaches such as Haar-like and Hog methods.

/pdf/moving-object-tracking-based-on-sparse-optical-flow-with-241spltz.pdf

Moving Object Tracking Based on Sparse Optical Flow with Moving Window and Target Estimator

Aqua farms will be the most frequently encountered obstacle when autonomous ships sail along the coastal area of Korea. We used YOLOv5 to create a model that detects aquaculture buoys. The distances between the buoys and the camera were calculated based on monocular and stereo vision using the detected image coordinates and compared with those from a laser distance sensor and radar. A dataset containing 2700 images of aquaculture buoys was divided between training and testing data in the ratio of 8:2. The trained model had precision, recall, and mAP of 0.936, 0.903, and 94.3%, respectively. Monocular vision calculates the distance based on camera position estimation and water surface coordinates of maritime objects, while stereo vision calculates the distance by finding corresponding points using SSD, NCC, and ORB and then calculating the disparity. The stereo vision had small error rates of −3.16% and −14.81% for short (NCC) and long distances (ORB); however, large errors were detected for objects located at a far distance. Monocular vision had error rates of 2.86% and −4.00% for short and long distances, respectively. Monocular vision is more effective than stereo vision for detecting maritime obstacles and can be employed as auxiliary sailing equipment along with radar.

/pdf/detecting-maritime-obstacles-using-camera-images-3rwpixlu.pdf

Detecting Maritime Obstacles Using Camera Images

https://www.koreascience.or.kr:443/article/JAKO202010763204245.pdf

Byung-Sun Kang

Papers

Moving Object Tracking Based on Sparse Optical Flow with Moving Window and Target Estimator

Detecting Maritime Obstacles Using Camera Images

A Study on Lashing Standards for Car Ferry Ships Sailing in Smooth Sea Areas

A Study on the Distance Error Correction of Maritime Object Detection System

A Study on the Development of YOLO-Based Maritime Object Detection System through Geometric Interpretation of Camera Images