{"title":"SeaTrack: Rethinking Observation-Centric SORT for Robust Nearshore Multiple Object Tracking","authors":"","doi":"10.1016/j.patcog.2024.111091","DOIUrl":null,"url":null,"abstract":"<div><div>Nearshore Multiple Object Tracking (NMOT) aims to locate and associate nearshore objects. Current approaches utilize Automatic Identification Systems (AIS) and radar to accomplish this task. However, video signals can describe the visual appearance of nearshore objects without prior information such as identity, location, or motion. In addition, sea clutter will not affect the capture of living objects by visual sensors. Recognizing this, we analyzed three key long-term challenges of the vision-based NMOT and proposed a tracking pipeline that relies solely on motion information. Maritime objects are highly susceptible to being obscured or submerged by waves, resulting in fragmented tracklets. We first introduced guiding modulation to address the long-term occlusion and interaction of maritime objects. Subsequently, we modeled confidence, altitude, and angular momentum to mitigate the effects of motion blur, ringing, and overshoot artifacts to observations in unstable imaging environments. Additionally, we designed a motion fusion mechanism that combines long-term macro tracklets with short-term fine-grained tracklets. This correction mechanism helps reduce the estimation variance of the Kalman Filter (KF) to alleviate the substantial nonlinear motion of maritime objects. We call this pipeline SeaTrack, which remains simple, online, and real-time, demonstrating excellent performance and scalability in benchmark evaluations.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0031320324008422","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Nearshore Multiple Object Tracking (NMOT) aims to locate and associate nearshore objects. Current approaches utilize Automatic Identification Systems (AIS) and radar to accomplish this task. However, video signals can describe the visual appearance of nearshore objects without prior information such as identity, location, or motion. In addition, sea clutter will not affect the capture of living objects by visual sensors. Recognizing this, we analyzed three key long-term challenges of the vision-based NMOT and proposed a tracking pipeline that relies solely on motion information. Maritime objects are highly susceptible to being obscured or submerged by waves, resulting in fragmented tracklets. We first introduced guiding modulation to address the long-term occlusion and interaction of maritime objects. Subsequently, we modeled confidence, altitude, and angular momentum to mitigate the effects of motion blur, ringing, and overshoot artifacts to observations in unstable imaging environments. Additionally, we designed a motion fusion mechanism that combines long-term macro tracklets with short-term fine-grained tracklets. This correction mechanism helps reduce the estimation variance of the Kalman Filter (KF) to alleviate the substantial nonlinear motion of maritime objects. We call this pipeline SeaTrack, which remains simple, online, and real-time, demonstrating excellent performance and scalability in benchmark evaluations.
期刊介绍:
The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.