Ship Classification Using Deep Learning Techniques for Maritime Target Tracking

Leclerc, Maxime; Florea, Mihai; Boury-Brisset, Anne-Claire; Kirubarajan, Thiagalingam; Duclos-Hindie, Nicolas

doi:10.23919/icif.2018.8455679

Cited by 63 publications

(31 citation statements)

References 28 publications

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“…Computer vision-based methods have shown many favorable results in the object tracking community [15][16][17][18] which presents its potential in addressing the above challenge (i.e., obtaining first-hand visual data). More specifically, image-based ship tracking models have attempted to extract distinct features to identify ship positions from maritime image sequences.…”

Section: Introductionmentioning

confidence: 99%

“…Hu et al [19] proposed a defogging framework for the purpose of removing fog interference in a single maritime image which may fail to obtain haze-removal maritime images in real-world applications. Leclerc et al [15] employed a deep convolution neural network to learn distinct ship features from maritime images which can be used for tracking ships in maritime images. We did not focus on deep learning model performance for ship tracking tasks, although they have shown many successes in general purpose computer vision tasks [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Chen

et al. 2020

Sensors

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Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritime safety improvement. Conventional models heavily rely on visual ship features for the purpose of tracking ships from maritime image sequences which may contain arbitrary tracking oscillations. To address this issue, we propose an ensemble ship tracking framework with a multi-view learning algorithm and wavelet filter model. First, the proposed model samples ship candidates with a particle filter following the sequential importance sampling rule. Second, we propose a multi-view learning algorithm to obtain raw ship tracking results in two steps: extracting a group of distinct ship contour relevant features (i.e., Laplacian of Gaussian, local binary pattern, Gabor filter, histogram of oriented gradient, and canny descriptors) and learning high-level intrinsic ship features by jointly exploiting underlying relationships shared by each type of ship contour features. Third, with the help of the wavelet filter, we performed a data quality control procedure to identify abnormal oscillations in the ship positions which were further corrected to generate the final ship tracking results. We demonstrate the proposed ship tracker’s performance on typical maritime traffic scenarios through four maritime surveillance videos.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Chen

et al. 2020

Sensors

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“…Cao et al made use of a CNN to extract the features from the vessel image and to eventually identify the ship in a frame of a video sequence [31]. Leclerc et al took full advantage of ship classification that is based on deep transfer learning to enhance the estimation capability of the trackers [32].…”

Section: Visible Vessel Detection and Trackingmentioning

confidence: 99%

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

et al. 2019

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It is crucial for unmanned surface vessels (USVs) to detect and track surrounding vessels in real time to avoid collisions at sea. However, the harsh maritime environment poses great challenges to multitarget tracking (MTT). In this paper, a novel tracking by detection framework that integrates the multimodel and multicue (M3C) pipeline is proposed, which aims at improving the detection and tracking performance. Regarding the multimodel, we predicted the maneuver probability of a target vessel via the gated recurrent unit (GRU) model with an attention mechanism, and fused their respective outputs as the output of a kinematic filter. We developed a hybrid affinity model based on multi cues, such as the motion, appearance, and attitude of the ego vessel in the data association stage. By using the proposed ship re-identification approach, the tracker had the capability of appearance matching via metric learning. Experimental evaluation of two public maritime datasets showed that our method achieved state-of-the-art performance, not only in identity switches (IDS) but also in frame rates.

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“…Transfer learning [41][42][43] is a common approach to handle lack of training data in a dataset. It is widely believed that networks trained on the ImageNet dataset [44] are able to learn general features from it; then, this network can be fine-tuned on other datasets for a specific task such as face recognition [45,46], classification [47][48][49], detection [50,51], and visual tracking [52][53][54]. Therefore, it makes transfer learning an essential approach, especially for the small datasets.…”

Section: Transfer Learningmentioning

confidence: 99%

Deep person re-identification in UAV images

Grigorev

Tian²,

Rho

et al. 2019

EURASIP J. Adv. Signal Process.

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The person re-identification is one of the most significant problems in computer vision and surveillance systems. The recent success of deep convolutional neural networks in image classification has inspired researchers to investigate the application of deep learning to the person re-identification. However, the huge amount of research on this problem considers classical settings, where pedestrians are captured by static surveillance cameras, although there is a growing demand for analyzing images and videos taken by drones. In this paper, we aim at filling this gap and provide insights on the person re-identification from drones. To our knowledge, it is the first attempt to tackle this problem under such constraints. We present the person re-identification dataset, named DRone HIT (DRHIT01), which is collected by using a drone. It contains 101 unique pedestrians, which are annotated with their identities. Each pedestrian has about 500 images. We propose to use a combination of triplet and large-margin Gaussian mixture (L-GM) loss to tackle the drone-based person re-identification problem. The proposed network equipped with multi-branch design, channel group learning, and combination of loss functions is evaluated on the DRHIT01 dataset. Besides, transfer learning from the most popular person re-identification datasets is evaluated. Experiment results demonstrate the importance of transfer learning and show that the proposed model outperforms the classic deep learning approach.

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Ship Classification Using Deep Learning Techniques for Maritime Target Tracking

Cited by 63 publications

References 28 publications

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

Deep person re-identification in UAV images

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