A Spatio-temporal Track Association Algorithm Based on Marine Vessel
Automatic Identification System Data
- URL: http://arxiv.org/abs/2010.15921v2
- Date: Thu, 23 Jun 2022 22:04:38 GMT
- Title: A Spatio-temporal Track Association Algorithm Based on Marine Vessel
Automatic Identification System Data
- Authors: Imtiaz Ahmed, Mikyoung Jun, Yu Ding
- Abstract summary: Tracking objects moving in real-time in a dynamic threat environment is important in national security and surveillance system.
To locate the anomalous pattern of movements, one needs to have an accurate data association algorithm.
We develop atemporal approach for tracking maritime vessels as the vessel's location and motion posing observations are collected by an Automatic Identification System.
- Score: 5.453186558530502
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Tracking multiple moving objects in real-time in a dynamic threat environment
is an important element in national security and surveillance system. It helps
pinpoint and distinguish potential candidates posing threats from other normal
objects and monitor the anomalous trajectories until intervention. To locate
the anomalous pattern of movements, one needs to have an accurate data
association algorithm that can associate the sequential observations of
locations and motion with the underlying moving objects, and therefore, build
the trajectories of the objects as the objects are moving. In this work, we
develop a spatio-temporal approach for tracking maritime vessels as the
vessel's location and motion observations are collected by an Automatic
Identification System. The proposed approach is developed as an effort to
address a data association challenge in which the number of vessels as well as
the vessel identification are purposely withheld and time gaps are created in
the datasets to mimic the real-life operational complexities under a threat
environment. Three training datasets and five test sets are provided in the
challenge and a set of quantitative performance metrics is devised by the data
challenge organizer for evaluating and comparing resulting methods developed by
participants. When our proposed track association algorithm is applied to the
five test sets, the algorithm scores a very competitive performance.
Related papers
- Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection [73.85890512959861]
We propose a task-agnostic framework to unify Salient Object Detection (SOD) and Camouflaged Object Detection (COD)
We design a simple yet effective contextual decoder involving the interval-layer and global context, which achieves an inference speed of 67 fps.
Experiments on public SOD and COD datasets demonstrate the superiority of our proposed framework in both supervised and unsupervised settings.
arXiv Detail & Related papers (2024-12-22T03:25:43Z) - Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime Environments [0.8796261172196743]
Vision-based target tracking is crucial for unmanned surface vehicles.
Real-time tracking in maritime environments is challenging due to dynamic camera movement, low visibility, and scale variation.
This study proposes a vision-guided object-tracking framework for USVs.
arXiv Detail & Related papers (2024-12-10T10:35:17Z) - SeMoLi: What Moves Together Belongs Together [51.72754014130369]
We tackle semi-supervised object detection based on motion cues.
Recent results suggest that motion-based clustering methods can be used to pseudo-label instances of moving objects.
We re-think this approach and suggest that both, object detection, as well as motion-inspired pseudo-labeling, can be tackled in a data-driven manner.
arXiv Detail & Related papers (2024-02-29T18:54:53Z) - A CNN-LSTM Architecture for Marine Vessel Track Association Using
Automatic Identification System (AIS) Data [2.094022863940315]
This study introduces a 1D CNN-LSTM architecture-based framework for track association.
The proposed framework takes the marine vessel's location and motion data collected through the Automatic Identification System (AIS) as input and returns the most likely vessel track as output in real-time.
arXiv Detail & Related papers (2023-03-24T15:26:49Z) - Performance Study of YOLOv5 and Faster R-CNN for Autonomous Navigation
around Non-Cooperative Targets [0.0]
This paper discusses how the combination of cameras and machine learning algorithms can achieve the relative navigation task.
The performance of two deep learning-based object detection algorithms, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLOv5) is tested.
The paper discusses the path to implementing the feature recognition algorithms and towards integrating them into the spacecraft Guidance Navigation and Control system.
arXiv Detail & Related papers (2023-01-22T04:53:38Z) - Real-Time Event-Based Tracking and Detection for Maritime Environments [1.6058099298620423]
Event cameras are ideal for object tracking applications due to their ability to capture fast-moving objects.
Existing event-based clustering and feature tracking approaches for surveillance and object detection work well in the majority of cases.
However, the maritime environment presents unique challenges such as the tendency of waves to produce the majority of events.
arXiv Detail & Related papers (2022-02-09T02:30:27Z) - SoDA: Multi-Object Tracking with Soft Data Association [75.39833486073597]
Multi-object tracking (MOT) is a prerequisite for a safe deployment of self-driving cars.
We propose a novel approach to MOT that uses attention to compute track embeddings that encode dependencies between observed objects.
arXiv Detail & Related papers (2020-08-18T03:40:25Z) - Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for
Online Collision Avoidance [95.86944752753564]
We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties.
Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates.
The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
arXiv Detail & Related papers (2020-07-28T07:34:30Z) - Benchmarking Unsupervised Object Representations for Video Sequences [111.81492107649889]
We compare the perceptual abilities of four object-centric approaches: ViMON, OP3, TBA and SCALOR.
Our results suggest that the architectures with unconstrained latent representations learn more powerful representations in terms of object detection, segmentation and tracking.
Our benchmark may provide fruitful guidance towards learning more robust object-centric video representations.
arXiv Detail & Related papers (2020-06-12T09:37:24Z) - Any Motion Detector: Learning Class-agnostic Scene Dynamics from a
Sequence of LiDAR Point Clouds [4.640835690336654]
We propose a novel real-time approach of temporal context aggregation for motion detection and motion parameters estimation.
We introduce an ego-motion compensation layer to achieve real-time inference with performance comparable to a naive odometric transform of the original point cloud sequence.
arXiv Detail & Related papers (2020-04-24T10:40:07Z) - Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for
Event-based Object Tracking [87.0297771292994]
We propose an Event-based Tracking-by-Detection (ETD) method for generic bounding box-based object tracking.
To achieve this goal, we present an Adaptive Time-Surface with Linear Time Decay (ATSLTD) event-to-frame conversion algorithm.
We compare the proposed ETD method with seven popular object tracking methods, that are based on conventional cameras or event cameras, and two variants of ETD.
arXiv Detail & Related papers (2020-02-13T15:58:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.