Vessel-speed Enforcement System by Multi-camera Detection and Re-identification

Groot, Herman G.J.; Zwemer, Matthijs H.; Wijnhoven, Rob G. J.; Bondarau, Egor

doi:10.5220/0008911202680277

Cited by 7 publications

(7 citation statements)

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“…The optimal training parameters for TriNet were investigated in previous work [ 2 ], leading to a learning-rate of

and 500 training epochs, while exponentially decaying the learning rate with a base of

starting at epoch 300. The system trained with these parameters resulted in a Rank-1 performance of

and

mAP on the Vessel-reID dataset, when applying the Default Query Approach.…”

Section: Experimental Results: Re-identificationmentioning

confidence: 99%

“…Compared to our previous work [2] presented at the VISAPP Conference (VISAPP Conference 2020, Valetta, Malta, http://www.visapp.visigrapp.org/ (accessed on 5 July 2021)), we have extended our experiments for detection by comparing with two other datasets and comparing different detection models. Furthermore, the re-ID experiments have been extended by including an additional re-ID model, several new extensions to our trajectory-based re-identification and an execution-time analysis.…”

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confidence: 99%

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Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

Zwemer

Groot

Wijnhoven

et al. 2021

Sensors

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This paper presents a camera-based vessel-speed enforcement system based on two cameras. The proposed system detects and tracks vessels per camera view and employs a re-identification (re-ID) function for linking vessels between the two cameras based on multiple bounding-box images per vessel. Newly detected vessels in one camera (query) are compared to the gallery set of all vessels detected by the other camera. To train and evaluate the proposed detection and re-ID system, a new Vessel-reID dataset is introduced. This extensive dataset has captured a total of 2474 different vessels covered in multiple images, resulting in a total of 136,888 vessel bounding-box images. Multiple CNN detector architectures are evaluated in-depth. The SSD512 detector performs best with respect to its speed (85.0% Recall@95Precision at 20.1 frames per second). For the re-ID of vessels, a large portion of the total trajectory can be covered by the successful detections of the SSD model. The re-ID experiments start with a baseline single-image evaluation obtaining a score of 55.9% Rank-1 (49.7% mAP) for the existing TriNet network, while the available MGN model obtains 68.9% Rank-1 (62.6% mAP). The performance significantly increases with 5.6% Rank-1 (5.7% mAP) for MGN by applying matching with multiple images from a single vessel. When emphasizing more fine details by selecting only the largest bounding-box images, another 2.0% Rank-1 (1.4% mAP) is added. Application-specific optimizations such as travel-time selection and applying a cross-camera matching constraint further enhance the results, leading to a final 88.9% Rank-1 and 83.5% mAP performance.

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“…The optimal training parameters for TriNet were investigated in previous work [ 2 ], leading to a learning-rate of

and 500 training epochs, while exponentially decaying the learning rate with a base of

starting at epoch 300. The system trained with these parameters resulted in a Rank-1 performance of

and

mAP on the Vessel-reID dataset, when applying the Default Query Approach.…”

Section: Experimental Results: Re-identificationmentioning

confidence: 99%

mentioning

confidence: 99%

Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

Zwemer

Groot

Wijnhoven

et al. 2021

Sensors

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“…On the common object dataset (MSCOCO) and two maritime-domain datasets (i.e., IPATCH and MarDCT), Heyse et al explored a domain-adaption method for the fine-grained identification of ship categories [55]. Groot et al re-identified target ships imaged from multiple cameras by matching vessel tracks and time filtering, among other methods [56]. They divided the known distance between fixed point cameras by the navigation time to estimate the target speed.…”

Section: Ship Recognition and Re-identificationmentioning

confidence: 99%

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Qiao

Liu

et al. 2021

JMSE

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The primary task of marine surveillance is to construct a perfect marine situational awareness (MSA) system that serves to safeguard national maritime rights and interests and to maintain blue homeland security. Progress in maritime wireless communication, developments in artificial intelligence, and automation of marine turbines together imply that intelligent shipping is inevitable in future global shipping. Computer vision-based situational awareness provides visual semantic information to human beings that approximates eyesight, which makes it likely to be widely used in the field of intelligent marine transportation. We describe how we combined the visual perception tasks required for marine surveillance with those required for intelligent ship navigation to form a marine computer vision-based situational awareness complex and investigated the key technologies they have in common. Deep learning was a prerequisite activity. We summarize the progress made in four aspects of current research: full scene parsing of an image, target vessel re-identification, target vessel tracking, and multimodal data fusion with data from visual sensors. The paper gives a summary of research to date to provide background for this work and presents brief analyses of existing problems, outlines some state-of-the-art approaches, reviews available mainstream datasets, and indicates the likely direction of future research and development. As far as we know, this paper is the first review of research into the use of deep learning in situational awareness of the ocean surface. It provides a firm foundation for further investigation by researchers in related fields.

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“…In [9], authors propose an architecture called the identity-oriented re-identification network that combines the triplet loss and softmax cross-entropy (CE) loss with a ResNet50 [13] architecture. In [11], authors base their work on [14] and extend the method with various multiquery strategies. In [29], authors introduce a new dataset, as well as a novel approach that employs global-and-local fusion-based discriminative feature learning.…”

Section: Person and Vehicle Re-identificationmentioning

confidence: 99%

Maritime vessel re-identification: novel VR-VCA dataset and a multi-branch architecture MVR-net

Ghahremani

Alkanat

Bondarev

2021

Machine Vision and Applications

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Maritime vessel re-identification (re-ID) is a computer vision task of vessel identity matching across disjoint camera views. Prominent applications of vessel re-ID exist in the fields of surveillance and maritime traffic flow analysis. However, the field suffers from the absence of a large-scale dataset that enables training of deep learning models. In this study, we present a new dataset that includes 4614 images of 729 vessels along with 5-bin orientation and 8-class vessel-type annotations to promote further research. A second contribution of this study is the baseline re-ID analysis of our new dataset. Performances of 10 recent deep learning architectures are quantitatively compared to reveal the best practices. Lastly, we propose a novel multi-branch deep learning architecture, Maritime Vessel Re-ID network (MVR-net), to address the challenging problem of vessel re-ID. Evaluation of our approach on the new dataset yields 74.5% mAP and 77.9% Rank-1 score, providing a performance increase of 5.7% mAP and 5.0% Rank-1 over the best-performing baseline. MVR-net also outperforms the PRN (a pioneering vehicle re-ID network), by 2.9% and 4.3% higher mAP and Rank-1, respectively.

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Vessel-speed Enforcement System by Multi-camera Detection and Re-identification

Cited by 7 publications

References 0 publications

Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Maritime vessel re-identification: novel VR-VCA dataset and a multi-branch architecture MVR-net

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