ARGOS-Venice Boat Classification

Bloisi, Domenico Daniele; Iocchi, Luca; Pennisi, Andrea; Tombolini, Luigi

doi:10.1109/avss.2015.7301727

Cited by 60 publications

(30 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the detection accuracy affects all the stages in the VPU process flow, it must be as high as possible, while maintaining an acceptable computational load. The three main components [24], while d) and e) are from the PASCAL VOC data set [20].…”

Section: Visual Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Automatic Maritime Surveillance Systems With Visual Information

Bloisi

Previtali

Pennisi

et al. 2017

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

Automatic surveillance systems for the maritime domain are becoming more and more important due to a constant increase of naval traffic and to the simultaneous reduction of crews on decks. However, available technology still provides only a limited support to this kind of applications. In this paper, a modular system for intelligent maritime surveillance, capable of fusing information from heterogeneous sources, is described. The system is designed to enhance the functions of the existing Vessel Traffic Services systems and to be deployable in populated areas, where radar-based systems cannot be used due to the high electromagnetic radiation emissions. A quantitative evaluation of the proposed approach has been carried out on a large and publicly available data set of images and videos, collected from multiple real sites, with different light, weather, and traffic conditions.

show abstract

Section: Visual Detectionmentioning

confidence: 99%

“…All data sets used in this experimental evaluation can be found at the MarDCT Maritime Detection, Classification and Tracking [24] database, containing images and videos with ground-truth annotations. The videos have been recorded with varying observing angles and weather conditions.…”

Section: A Data Setmentioning

confidence: 99%

Enhancing Automatic Maritime Surveillance Systems With Visual Information

Bloisi

Previtali

Pennisi

et al. 2017

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…A database of surveillance videos and image sequences, dedicated to the maritime domain, is MarDCT -Maritime Detection, Classification, and Tracking data set (Bloisi et al, 2015). MarDCT has been developed for evaluating BS techniques on environments characterized by water background and for providing very challenging data (containing reflections, occlusions, waves, and wakes) from real working systems.…”

Section: Related Workmentioning

confidence: 99%

Parallel multi-modal background modeling

Bloisi

Pennisi

Iocchi

2017

Pattern Recognition Letters

Self Cite

View full text Add to dashboard Cite

Background subtraction is a widely used technique for detecting moving objects in image sequences. Very often background subtraction approaches assume the availability of one or more clear (i.e., without foreground objects) frames at the beginning of the sequence in input. However, this assumption is not always true, especially when dealing with dynamic background or crowded scenes. In this paper, we present the results of a multi-modal background modeling method that is able to generate a reliable initial background model even if no clear frames are available. The proposed algorithm runs in realtime on HD images. Quantitative experiments have been conducted taking into account six different quality metrics on a set of 14 publicly available image sequences. The obtained results demonstrate a high-accuracy in generating the background model in comparison with several other methods.

show abstract

“…Compared to the remote sensing datasets and infrared (IR) datasets [2], the electro-optical visible dataset is insufficiently applied to maritime environments [6], [7]. The majority of maritime visible classification datasets is closed source to difficultly obtain, such as the Maritime Detection Classification and Tracking (MarDCT) datasets [11] and E2S2-Vessel dataset [12]. Besides, rare categories are hard to collect, and a few instances toughly train a deep CNNs of a satisfying accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…However, the CNN application of the ship classification in maritime environments began in 2015 [13]. Due to the shortage of well-labeled dataset and robust algorithm or specific benchmark process, the majority of solutions dwell on coarse-grained classification instead of fine-grained or instance-level image classification [11], [14], [18], [19]. Numerous approaches remain the design and improved CNN model based on AlexNet and VGGNet [4], [12]- [14], [18]- [21].…”

Section: Introductionmentioning

confidence: 99%

Maritime Visible Image Classification Based on Double Transfer Method

et al. 2020

View full text Add to dashboard Cite

Image classification using deep transfer learning has received significant attention, benefiting from pre-trained with the large-scale annotation dataset and continuous improvement of neural network structure. In contrast to universal image classification, however, few publicly available datasets of maritime environments utilize deep transfer learning. Due to data-gathering effort and computational cost, the maritime datasets are deficient in the method of merging datasets and the benchmark of few-shot dataset classifiers. In this paper, we proposed the double transfer method, consisting of the merging datasets network and the backbone network, to address the problem. The merging datasets network measuring image similarity separates classes of known and unknown samples to reorganize a dataset, and the backbone network is constructed from the model EfficientNet-b5 by network-based deep transfer learning. Using the merging datasets network, we introduce the visible maritime image dataset, which has 3,750 images and twenty-five classes, including multitudinous maritime objects. The backbone networks evaluated and analyzed the dataset based on accuracy, precision, recall, and F-measure metrics. Using the double transfer method, we can achieve an accuracy of 91.39% in the visible maritime image dataset.

show abstract

ARGOS-Venice Boat Classification

Cited by 60 publications

References 5 publications

Enhancing Automatic Maritime Surveillance Systems With Visual Information

Enhancing Automatic Maritime Surveillance Systems With Visual Information

Parallel multi-modal background modeling

Maritime Visible Image Classification Based on Double Transfer Method

Contact Info

Product

Resources

About