scholarly journals Robust Template Adjustment Siamese Network for Object Visual Tracking

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1466
Author(s):  
Chuanming Tang ◽  
Peng Qin ◽  
Jianlin Zhang
Keyword(s):  
Feature Extraction ◽  
Visual Tracking ◽  
State Of The Art ◽  
Tracking Problem ◽  
Training Process ◽  
Current Frame ◽  
Siamese Network ◽  
Efficient Learning ◽  
Siamese Networks

Most of the existing trackers address the visual tracking problem by extracting an appearance template from the first frame, which is used to localize the target in the current frame. Unfortunately, they typically face the model degeneration challenge, which easily results in model drift and target loss. To address this issue, a novel Template Adjustment Siamese Network (TA-Siam) is proposed in this paper. The proposed framework TA-Siam consists of two simple subnetworks: The template adjustment subnetwork for feature extraction and the classification-regression subnetwork for bounding box prediction. The template adjustment module adaptively uses the feature of subsequent frames to adjust the current template. It makes the template adapt to the target appearance variation of long-term sequence and effectively overcomes model drift problem of Siamese networks. In order to reduce classification errors, the rhombus labels are proposed in our TA-Siam. For more efficient learning and faster convergence, our proposed tracker uses a more effective regression loss in the training process. Extensive experiments and comparisons with trackers are conducted on the challenging benchmarks including VOT2016, VOT2018, OTB50, OTB100, GOT-10K, and LaSOT. Our TA-Siam achieves state-of-the-art performance at the speed of 45 FPS.

Robust Inland Waterway Ship Tracking via Hybrid TLD and Kalman Filter

2014 ◽  
Vol 1037 ◽  
pp. 373-377 ◽  
Author(s):  
Teng Fei ◽  
Liu Qing ◽  
Lin Zhu ◽  
Jing Li
Keyword(s):  
Kalman Filter ◽  
Visual Tracking ◽  
State Of The Art ◽  
Experimental Results ◽  
Closed Circuit ◽  
Video Sequences ◽  
Inland Waterway ◽  
Cluttered Background ◽  
Full Occlusion

In this paper, we mainly address the problem of tracking a single ship in inland waterway CCTV (Closed-Circuit Television) video sequences. Although state-of-the-art performance has been demonstrated in TLD (Tracking-Learning-Detection) visual tracking, it is still challenging to perform long-term robust ship tracking due to factors such as cluttered background, scale change, partial or full occlusion and so forth. In this work, we focus on tracking a single ship when it suffers occlusion. To accomplish this goal, an effective Kalman filter is adopted to construct a novel online model to adapt to the rapid ship appearance change caused by occlusion. Experimental results on numerous inland waterway CCTV video sequences demonstrate that the proposed algorithm outperforms the original one.

scholarly journals Scattering Transform for Classification in Non-Intrusive Load Monitoring

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6796
Author(s):  
Everton Luiz de Aguiar ◽  
André Eugenio Lazzaretti ◽  
Bruna Machado Mulinari ◽  
Daniel Rodrigues Pipa
Keyword(s):  
Feature Extraction ◽  
Data Augmentation ◽  
State Of The Art ◽  
Training Process ◽  
Convolutional Network ◽  
Scattering Transform ◽  
Augmentation Strategies ◽  
Load Monitoring ◽  
Event Location ◽  
Traditional Approaches

Nonintrusive Load Monitoring (NILM) uses computational methods to disaggregate and classify electrical appliances signals. The classification is usually based on the power signatures of the appliances obtained by a feature extractor. State-of-the-art results were obtained extracting NILM features with convolutional neural networks (CNN). However, it depends on the training process with large datasets or data augmentation strategies. In this paper, we propose a feature extraction strategy for NILM using the Scattering Transform (ST). The ST is a convolutional network analogous to CNN. Nevertheless, it does not need a training process in the feature extraction stage, and the filter coefficients are analytically determined (not empirically, like CNN). We perform tests with the proposed method on different publicly available datasets and compare the results with state-of-the-art deep learning-based and traditional approaches (including wavelet transform and V-I representations). The results show that ST classification accuracy is more robust in terms of waveform parameters, such as signal length, sampling frequency, and event location. Besides, ST overcame the state-of-the-art techniques for single and aggregated loads (accuracies above 99% for all evaluated datasets), in different training scenarios with single and aggregated loads, indicating its feasibility in practical NILM scenarios.

scholarly journals Object Relocation Visual Tracking Based On Histogram Filter And Siamese Network

2022 ◽  
Author(s):  
Jianlong Zhang ◽  
Qiao Li ◽  
Bin Wang ◽  
Chen Chen ◽  
Tianhong Wang ◽  
...  
Keyword(s):  
Visual Tracking ◽  
Image Matching ◽  
Euclidean Distance ◽  
State Of The Art ◽  
Feature Representation ◽  
Tracking Accuracy ◽  
Match Filter ◽  
Siamese Network ◽  
Matching Process ◽  
Dynamic Template

Abstract Siamese network based trackers formulate the visual tracking mission as an image matching process by regression and classification branches, which simplifies the network structure and improves tracking accuracy. However, there remain many problems as described below. 1) The lightweight neural networks decreases feature representation ability. The tracker is easy to fail under the disturbing distractors (e.g., deformation and similar objects) or large changes in viewing angle. 2) The tracker cannot adapt to variations of the object. 3) The tracker cannot reposition the object that has failed to track. To address these issues, we first propose a novel match filter arbiter based on the Euclidean distance histogram between the centers of multiple candidate objects to automatically determine whether the tracker fails. Secondly, Hopcroft-Karp algorithm is introduced to select the winners from the dynamic template set through the backtracking process, and object relocation is achieved by comparing the Gradient Magnitude Similarity Deviation between the template and the winners. The experiments show that our method obtains better performance on several tracking benchmarks, i.e., OTB100, VOT2018, GOT-10k and LaSOT, compared with state-of-the-art methods.

scholarly journals SNS-CF: Siamese Network with Spatially Semantic Correlation Features for Object Tracking

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4881
Author(s):  
Thierry Ntwari ◽  
Hasil Park ◽  
Joongchol Shin ◽  
Joonki Paik
Keyword(s):  
Object Tracking ◽  
State Of The Art ◽  
Input Image ◽  
Frame Rate ◽  
Correlation Filter ◽  
Correlation Filters ◽  
Semantic Correlation ◽  
Siamese Network ◽  
Feature Extractor ◽  
Siamese Networks

Recent advances in object tracking based on deep Siamese networks shifted the attention away from correlation filters. However, the Siamese network alone does not have as high accuracy as state-of-the-art correlation filter-based trackers, whereas correlation filter-based trackers alone have a frame update problem. In this paper, we present a Siamese network with spatially semantic correlation features (SNS-CF) for accurate, robust object tracking. To deal with various types of features spread in many regions of the input image frame, the proposed SNS-CF consists of—(1) a Siamese feature extractor, (2) a spatially semantic feature extractor, and (3) an adaptive correlation filter. To the best of authors knowledge, the proposed SNS-CF is the first attempt to fuse the Siamese network and the correlation filter to provide high frame rate, real-time visual tracking with a favorable tracking performance to the state-of-the-art methods in multiple benchmarks.

scholarly journals Long-term target tracking combined with re-detection

2020 ◽  
Author(s):  
ZengShun Zhao ◽  
Juanjuan Wang ◽  
HaoRan Yang ◽  
Ning Xu ◽  
Chengqin Wu ◽  
...  
Keyword(s):  
Visual Tracking ◽  
Correlation Energy ◽  
State Of The Art ◽  
Tracking Algorithm ◽  
Correlation Filters ◽  
Short Term ◽  
Tracking Method ◽  
Svm Model ◽  
Confidence Degree

Abstract The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, most existing methods have not been done and their performances have also been limited. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Discriminative Correlation Filters (DCF) tracking algorithm with a re-detection component based on the SVM model. The DCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 dataset, the experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

scholarly journals Deeper Siamese network with multi‐level feature fusion for real‐time visual tracking

2019 ◽  
Vol 55 (13) ◽  
pp. 742-745 ◽  
Author(s):  
Kang Yang ◽  
Huihui Song ◽  
Kaihua Zhang ◽  
Jiaqing Fan
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Feature Fusion ◽  
Siamese Network ◽  
Multi Level

scholarly journals Diagnostic Challenges on the Laboratory Detection of Lupus Anticoagulant

Biomedicines ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 844
Author(s):  
Armando Tripodi
Keyword(s):  
Lupus Anticoagulant ◽  
Clinical Laboratory ◽  
External Quality Assessment ◽  
State Of The Art ◽  
Diagnostic Procedures ◽  
External Quality ◽  
Glycoprotein I ◽  
Current State ◽  
Clinical Laboratories

Lupus anticoagulant (LA) is one of the three laboratory parameters (the others being antibodies to either cardiolipin or β2-glycoprotein I) which defines the rare but potentially devastating condition known as antiphospholipid syndrome (APS). Testing for LA is a challenging task for the clinical laboratory because specific tests for its detection are not available. However, proper LA detection is paramount for patients’ management, as its persistent positivity in the presence of (previous or current) thrombotic events, candidate for long term anticoagulation. Guidelines for LA detection have been established and updated over the last two decades. Implementation of these guidelines across laboratories and participation to external quality assessment schemes are required to help standardize the diagnostic procedures and help clinicians for appropriate management of APS. This article aims to review the current state of the art and the challenges that clinical laboratories incur in the detection of LA.

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

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