A Data Set for Airborne Maritime Surveillance Environments

Ribeiro, Ricardo; Cruz, Gonçalo; Matos, Jorge; Bernardino, Alexandre

doi:10.1109/tcsvt.2017.2775524

Cited by 58 publications

(31 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…MarDCT [10] was collected by a surveillance system and contained only nine videos of ships. The Seagull Dataset [11] provided a rich dataset for ship tracking. The videos in the seagull dataset were collected by an airplane, and although there were 19 videos covering six ship types, it had fewer objects in each category.…”

Section: ) Ship Datasetsmentioning

confidence: 99%

“…Nevertheless, only nine videos were included in the dataset, and the video resolution was relatively low. A larger dataset, the Seagull Dataset [11], was released in 2017, and included 150,000 images and was well labeled. However, most videos contain only one target with simple ocean backgrounds, and the ability of the tracking models to deal with complex tracking scenarios cannot be evaluated, such as cluttered backgrounds and occlusion.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

LMD-TShip^⋆: Vision Based Large-Scale Maritime Ship Tracking Benchmark for Autonomous Navigation Applications

et al. 2021

View full text Add to dashboard Cite

Accurate ship tracking is very important for the security of maritime activities, especially the raising requirements of autonomous navigation applications, e.g., autonomous surface vehicles (ASVs). Unlike deep-learning-based object-tracking methods are prevailing in autonomous driving because of good environmental robustness and high tracking accuracy, few deep-tracking models can be found for maritime ships. The main reason for that is the lack of qualified ship datasets, especially datasets with shipbased perspectives. Therefore, a large-scale, high-definition dataset for ship tracking, LMD-TShip (Large Maritime Dataset), is provided in this paper. In this dataset, five types of ships are included, from cargo ships, fishing ships, passenger ships, and speed boats to unmanned ships. Specifically, LMD-TShip consists of 40,240 frames in 191 videos, each of which is carefully and manually annotated with bounding boxes in YOLO format. Moreover, 13 attributes are used to label videos, e.g., scale variation (SV), occlusion (OCC), basically covering tracking challenges of maritime ship tracking. Next, a detailed analysis is carried out to demonstrate the characteristics of LMD-TShip. Finally, experiments with five baseline short-term tracking models on the dataset are performed, e.g., ECO, SiamRPN++, and the experimental results demonstrate its good evaluation ability, which will provide effective means for training and testing tracking models related to maritime ships.

show abstract

Section: ) Ship Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LMD-TShip^⋆: Vision Based Large-Scale Maritime Ship Tracking Benchmark for Autonomous Navigation Applications

et al. 2021

View full text Add to dashboard Cite

show abstract

“…We examined the following UAV datasets: UCF's dataset (http://crcv.ucf.edu/data/UCF_Aerial_ Action.php), VIRAT [51], MRP [52], the privacy-based mini-drones dataset [53], the aerial videos dataset described in [54], UAV123 [55], DTB70 [57], Okutama-Action [58], VisDrone [64], CARPK [59], SEAGULL [60], DroneFace [61], and the aerial video dataset described in [56]). A total of 43 videos (RGB, 30 frame per second (fps), 1280 × 720 or 720 × 480) were selected from databases VIRAT, UAV123, and DTB70.…”

Section: Content Selectionmentioning

confidence: 99%

“…While it is now rather easy to find eye tracking data on typical images [35,[37][38][39][40][41][42][43][44][45] or videos [46][47][48][49][50], and that there are many UAV content datasets [7,[51][52][53][54][55][56][57][58][59][60][61][62], it turns out to be extremely difficult to find eye-tracking data on UAV content. This is even truer when we consider dynamic salience, which refers to salience for video content.…”

mentioning

confidence: 99%

EyeTrackUAV2: A Large-Scale Binocular Eye-Tracking Dataset for UAV Videos

Perrin

Krassanakis

Zhang

et al. 2020

Drones

View full text Add to dashboard Cite

The fast and tremendous evolution of the unmanned aerial vehicle (UAV) imagery gives place to the multiplication of applications in various fields such as military and civilian surveillance, delivery services, and wildlife monitoring. Combining UAV imagery with study of dynamic salience further extends the number of future applications. Indeed, considerations of visual attention open the door to new avenues in a number of scientific fields such as compression, retargeting, and decision-making tools. To conduct saliency studies, we identified the need for new large-scale eye-tracking datasets for visual salience in UAV content. Therefore, we address this need by introducing the dataset EyeTrackUAV2. It consists of the collection of precise binocular gaze information (1000 Hz) over 43 videos (RGB, 30 fps, 1280 × 720 or 720 × 480). Thirty participants observed stimuli under both free viewing and task conditions. Fixations and saccades were then computed with the dispersion-threshold identification (I-DT) algorithm, while gaze density maps were calculated by filtering eye positions with a Gaussian kernel. An analysis of collected gaze positions provides recommendations for visual salience ground-truth generation. It also sheds light upon variations of saliency biases in UAV videos when opposed to conventional content, especially regarding the center bias.

show abstract

“…Comba et al [12] proposed using the unmanned aerial vehicle (UAV) multi-spectral generated 3D point cloud image to accurately detect vineyards, which plays a vital crop monitoring function in the viticulture process. Ribeiro et al [13] presented a dataset with surveillance imagery over the sea that was captured by a small size UAV. This dataset presents object examples ranging from cargo ships, small boats, life rafts to hydrocarbon slick.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification

Xiu

Wen²,

Yuan

et al. 2019

Sensors

View full text Add to dashboard Cite

In order to monitor and manage vessels in channels effectively, identification and tracking are very necessary. This work developed a maritime unmanned aerial vehicle (Mar-UAV) system equipped with a high-resolution camera and an Automatic Identification System (AIS). A multi-feature and multi-level matching algorithm using the spatiotemporal characteristics of aerial images and AIS information was proposed to detect and identify field vessels. Specifically, multi-feature information, including position, scale, heading, speed, etc., are used to match between real-time image and AIS message. Additionally, the matching algorithm is divided into two levels, point matching and trajectory matching, for the accurate identification of surface vessels. Through such a matching algorithm, the Mar-UAV system is able to automatically identify the vessel’s vision, which improves the autonomy of the UAV in maritime tasks. The multi-feature and multi-level matching algorithm has been employed for the developed Mar-UAV system, and some field experiments have been implemented in the Yangzi River. The results indicated that the proposed matching algorithm and the Mar-UAV system are very significant for achieving autonomous maritime supervision.

show abstract

A Data Set for Airborne Maritime Surveillance Environments

Cited by 58 publications

References 25 publications

LMD-TShip^⋆: Vision Based Large-Scale Maritime Ship Tracking Benchmark for Autonomous Navigation Applications

LMD-TShip^⋆: Vision Based Large-Scale Maritime Ship Tracking Benchmark for Autonomous Navigation Applications

EyeTrackUAV2: A Large-Scale Binocular Eye-Tracking Dataset for UAV Videos

A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification

Contact Info

Product

Resources

About

A Data Set for Airborne Maritime Surveillance Environments

Cited by 58 publications

References 25 publications

LMD-TShip⋆: Vision Based Large-Scale Maritime Ship Tracking Benchmark for Autonomous Navigation Applications

LMD-TShip⋆: Vision Based Large-Scale Maritime Ship Tracking Benchmark for Autonomous Navigation Applications

EyeTrackUAV2: A Large-Scale Binocular Eye-Tracking Dataset for UAV Videos

A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification

Contact Info

Product

Resources

About

LMD-TShip^⋆: Vision Based Large-Scale Maritime Ship Tracking Benchmark for Autonomous Navigation Applications

LMD-TShip^⋆: Vision Based Large-Scale Maritime Ship Tracking Benchmark for Autonomous Navigation Applications