scholarly journals Parallel Three-Branch Correlation Filters for Complex Marine Environmental Object Tracking Based on a Confidence Mechanism

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5210
Author(s):  
Yihong Zhang ◽  
Shuai Li ◽  
Demin Li ◽  
Wuneng Zhou ◽  
Yijin Yang ◽  
...  
Keyword(s):  
Object Tracking ◽  
Marine Environment ◽  
Correlation Filter ◽  
Sea Waves ◽  
Correlation Filters ◽  
Position Change ◽  
Current Frame ◽  
Environmental Object ◽  
The Impact ◽  
Marine Environmental

Marine object tracking is critical for search and rescue activities in the complex marine environment. However, the complex marine environment poses a huge challenge to the effect of tracking, such as the variability of light, the impact of sea waves, the occlusion of other ships, etc. Under these complex marine environmental factors, how to design an efficient dynamic visual tracker to make the results accurate, real time and robust is particularly important. The parallel three-branch correlation filters for complex marine environmental object tracking based on a confidence mechanism is proposed by us. The proposed tracker first detects the appearance change and position change of the object by constructing parallel three-branch correlation filters, which enhances the robustness of the correlation filter model. Through the weighted fusion of response maps, the center position of the object is accurately located. Secondly, the Gaussian-triangle joint distribution is used to replace the original Gaussian distribution in the training phase. Finally, a verification mechanism of confidence metric is embedded in the filter update section to analyze the tracking effect of the current frame, and to update the filter sample from verification result. Thus, a more accurate correlation filter is trained to prevent model drift and achieve a good tracking effect. We found that the effect of various interferences on the filter is effectively reduced by comparing with other trackers. The experiments prove that the proposed tracker can play an outstanding role in the complex marine environment.

scholarly journals Parallel Correlation Filters for Real-Time Visual Tracking

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2362 ◽  
Author(s):  
Yijin Yang ◽  
Yihong Zhang ◽  
Demin Li ◽  
Zhijie Wang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Research Field ◽  
Tracking Performance ◽  
Correlation Filter ◽  
Visual Object ◽  
Correlation Filters ◽  
Illumination Variation ◽  
Visual Object Tracking ◽  
Appearance Changes

Correlation filter-based methods have recently performed remarkably well in terms of accuracy and speed in the visual object tracking research field. However, most existing correlation filter-based methods are not robust to significant appearance changes in the target, especially when the target undergoes deformation, illumination variation, and rotation. In this paper, a novel parallel correlation filters (PCF) framework is proposed for real-time visual object tracking. Firstly, the proposed method constructs two parallel correlation filters, one for tracking the appearance changes in the target, and the other for tracking the translation of the target. Secondly, through weighted merging the response maps of these two parallel correlation filters, the proposed method accurately locates the center position of the target. Finally, in the training stage, a new reasonable distribution of the correlation output is proposed to replace the original Gaussian distribution to train more accurate correlation filters, which can prevent the model from drifting to achieve excellent tracking performance. The extensive qualitative and quantitative experiments on the common object tracking benchmarks OTB-2013 and OTB-2015 have demonstrated that the proposed PCF tracker outperforms most of the state-of-the-art trackers and achieves a high real-time tracking performance.

scholarly journals Learning Local–Global Multiple Correlation Filters for Robust Visual Tracking with Kalman Filter Redetection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1129 ◽  
Author(s):  
Jianming Zhang ◽  
Yang Liu ◽  
Hehua Liu ◽  
Jin Wang
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Correlation Filter ◽  
Appearance Model ◽  
Visual Object ◽  
Multiple Correlation ◽  
Correlation Filters ◽  
Tracking Algorithms ◽  
Global Correlation ◽  
Object Appearance

Visual object tracking is a significant technology for camera-based sensor networks applications. Multilayer convolutional features comprehensively used in correlation filter (CF)-based tracking algorithms have achieved excellent performance. However, there are tracking failures in some challenging situations because ordinary features are not able to well represent the object appearance variations and the correlation filters are updated irrationally. In this paper, we propose a local–global multiple correlation filters (LGCF) tracking algorithm for edge computing systems capturing moving targets, such as vehicles and pedestrians. First, we construct a global correlation filter model with deep convolutional features, and choose horizontal or vertical division according to the aspect ratio to build two local filters with hand-crafted features. Then, we propose a local–global collaborative strategy to exchange information between local and global correlation filters. This strategy can avoid the wrong learning of the object appearance model. Finally, we propose a time-space peak to sidelobe ratio (TSPSR) to evaluate the stability of the current CF. When the estimated results of the current CF are not reliable, the Kalman filter redetection (KFR) model would be enabled to recapture the object. The experimental results show that our presented algorithm achieves better performances on OTB-2013 and OTB-2015 compared with the other latest 12 tracking algorithms. Moreover, our algorithm handles various challenges in object tracking well.

scholarly journals Managing Marine Environmental Pollution using Artificial Intelligence

2021 ◽  
Vol 3 (2) ◽  
pp. Manuscript
Author(s):  
NItin Agarwala
Keyword(s):  
Artificial Intelligence ◽  
Environmental Pollution ◽  
Marine Environment ◽  
Fossil Fuels ◽  
Industrial Revolution ◽  
Marine Pollution ◽  
The Sustainable Development ◽  
The Impact ◽  
Marine Environmental ◽  
Marine Environmental Pollution

The marine environment has deteriorated to an extent that it has begun to impact human health and the planet itself.  The primary cause of this deterioration as identified are, an increasing population, the industrial revolution and the increased use of fossil fuels.  While the damage done to the environment cannot be undone, the impact can be lessened by better understanding the ocean and monitoring future pollution using technology.  Such an effort will help achieve sustainability as laid out by the Sustainable Development Goals 2030 of the United Nations.  The article aims to provide an insight into one such technology, namely ‘Artificial Intelligence (AI)’, being developed to understand and monitor the marine pollution.  In doing so the article will discuss the emerging opportunities and risks associated with the use of AI in managing marine environmental pollution through sustainability.  To strengthen the argument, use-cases of AI in the marine environment and their scalability are discussed.

scholarly journals Improved Hierarchical Convolutional Features for Robust Visual Object Tracking

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jinping Sun
Keyword(s):  
Object Tracking ◽  
Target Position ◽  
Feature Representation ◽  
Correlation Filter ◽  
Low Rank ◽  
Visual Object ◽  
Threshold Condition ◽  
Current Frame ◽  
Visual Object Tracking

The target and background will change continuously in the long-term tracking process, which brings great challenges to the accurate prediction of targets. The correlation filter algorithm based on manual features is difficult to meet the actual needs due to its limited feature representation ability. Thus, to improve the tracking performance and robustness, an improved hierarchical convolutional features model is proposed into a correlation filter framework for visual object tracking. First, the objective function is designed by lasso regression modeling, and a sparse, time-series low-rank filter is learned to increase the interpretability of the model. Second, the features of the last layer and the second pool layer of the convolutional neural network are extracted to realize the target position prediction from coarse to fine. In addition, using the filters learned from the first frame and the current frame to calculate the response maps, respectively, the target position is obtained by finding the maximum response value in the response map. The filter model is updated only when these two maximum responses meet the threshold condition. The proposed tracker is evaluated by simulation analysis on TC-128/OTB2015 benchmarks including more than 100 video sequences. Extensive experiments demonstrate that the proposed tracker achieves competitive performance against state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.829 and 0.695, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes.

scholarly journals rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking

2020 ◽  
Vol 10 (9) ◽  
pp. 3021
Author(s):  
Wangpeng He ◽  
Heyi Li ◽  
Wei Liu ◽  
Cheng Li ◽  
Baolong Guo
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Target Position ◽  
Color Histogram ◽  
Correlation Filter ◽  
Correlation Filters ◽  
Training Samples ◽  
Model Update ◽  
Score Map ◽  
Scale Detection

Object tracking is a challenging research task because of drastic appearance changes of the target and a lack of training samples. Most online learning trackers are hampered by complications, e.g., drifting problem under occlusion, being out of view, or fast motion. In this paper, a real-time object tracking algorithm termed “robust sum of template and pixel-wise learners” (rStaple) is proposed to address those problems. It combines multi-feature correlation filters with a color histogram. Firstly, we extract a combination of specific features from the searching area around the target and then merge feature channels to train a translation correlation filter online. Secondly, the target state is determined by a discriminating mechanism, wherein the model update procedure stops when the target is occluded or out of view, and re-activated when the target re-appears. In addition, by calculating the color histogram score in the searching area, a significant enhancement is adopted for the score map. The target position can be estimated by combining the enhanced color histogram score with the correlation filter response map. Finally, a scale filter is trained for multi-scale detection to obtain the final tracking result. Extensive experimental results on a large benchmark dataset demonstrates that the proposed rStaple is superior to several state-of-the-art algorithms in terms of accuracy and efficiency.

scholarly journals Motion-Aware Correlation Filters for Online Visual Tracking

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3937 ◽  
Author(s):  
Yihong Zhang ◽  
Yijin Yang ◽  
Wuneng Zhou ◽  
Lifeng Shi ◽  
Demin Li
Keyword(s):  
Object Tracking ◽  
Visual Tracking ◽  
Model Updating ◽  
Visual Object ◽  
Adaptive Model ◽  
Correlation Filters ◽  
Current Frame ◽  
Visual Object Tracking ◽  
Fast Motion ◽  
Better Than

The discriminative correlation filters-based methods struggle deal with the problem of fast motion and heavy occlusion, the problem can severely degrade the performance of trackers, ultimately leading to tracking failures. In this paper, a novel Motion-Aware Correlation Filters (MACF) framework is proposed for online visual object tracking, where a motion-aware strategy based on joint instantaneous motion estimation Kalman filters is integrated into the Discriminative Correlation Filters (DCFs). The proposed motion-aware strategy is used to predict the possible region and scale of the target in the current frame by utilizing the previous estimated 3D motion information. Obviously, this strategy can prevent model drift caused by fast motion. On the base of the predicted region and scale, the MACF detects the position and scale of the target by using the DCFs-based method in the current frame. Furthermore, an adaptive model updating strategy is proposed to address the problem of corrupted models caused by occlusions, where the learning rate is determined by the confidence of the response map. The extensive experiments on popular Object Tracking Benchmark OTB-100, OTB-50 and unmanned aerial vehicles (UAV) video have demonstrated that the proposed MACF tracker performs better than most of the state-of-the-art trackers and achieves a high real-time performance. In addition, the proposed approach can be integrated easily and flexibly into other visual tracking algorithms.

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 The Impact of Offshore Environment on Drillship Positioning and Operations during Drilling the First Exploratory Well in Lebanon

2021 ◽  
Vol 5 (3) ◽  
pp. 1-11
Author(s):  
Halafawi M
Keyword(s):  
Marine Environment ◽  
Environmental Conditions ◽  
Weather Forecasting ◽  
Impact Loads ◽  
Sea Waves ◽  
Offshore Drilling ◽  
Drilling Operations ◽  
The Impact

The offshore drilling activities in Lebanon have been started since 2020. All studies regarding the drilling operations and the environmental conditions become inevitable. Therefore, the objective of our paper is to study the marine environment in the region where a drillship called TUX was used to drill the first exploratory well in Lebanon. The effect of wind and wave on the drillship was studied using two predictive techniques: WW3 and ERA5. Furthermore, both techniques were used in order to predict the climate environmental conditions during the period where the TUX are positioned and performed drilling Well B4-1 in Block 4. The computed conditions of weather forecasting are wave and wind lengths, speeds, and heights. Spider plots were constructed in order to show the behavior of the sea waves during positioning and operating the TUX rig. Additionally, the impact loads produced from the wind was computed using several models and methods. A comparison between these methods was also done to show the accuracy of models. Lastly, the developed studies are very usefully so as to determine the safe conditions and periods for positioning the drillship to keep its stability.

The impact of urban effluents on the coastal marine environment of Mediterranean islands

1995 ◽  
Vol 32 (9-10) ◽  
pp. 85-94 ◽  
Author(s):  
Michael O. Angelidis
Keyword(s):  
Heavy Metals ◽  
Organic Matter ◽  
Marine Environment ◽  
Suspended Particle ◽  
Pollution Source ◽  
Mediterranean Islands ◽  
Coastal Marine Environment ◽  
Coastal Marine ◽  
The Impact ◽  
The City

The impact of the urban effluents of Mytilene (Lesvos island, Greece) on the receiving coastal marine environment, was evaluated by studying the quality of the city effluents (BOD5, COD, SS, heavy metals) and the marine sediments (grain size, organic matter, heavy metals). It was found that the urban effluents of Mytilene contain high organic matter and suspended particle load because of septage discharge into the sewerage network. Furthermore, although the city does not host important industrial activity, its effluents contain appreciable metal load, which is mainly associated with the particulate phase. The city effluents are discharged into the coastal marine environment and their colloidal and particulate matter after flocculation settles to the bottom, where is incorporated into the sediments. Over the years, the accumulation of organic matter and metals into the harbour mud has created a non-point pollution source in the relatively non-polluted coastal marine environment of the island. Copper and Zn were the metals which presented the higher enrichment in the sediments of the inner harbour of Mytilene.

Rotation adaptive correlation filter for moving object tracking in satellite videos

2021 ◽  
Vol 438 ◽  
pp. 94-106
Author(s):  
Shiyu Xuan ◽  
Shengyang Li ◽  
Zifei Zhao ◽  
Zhuang Zhou ◽  
Wanfeng Zhang ◽  
...  
Keyword(s):  
Object Tracking ◽  
Moving Object ◽  
Correlation Filter ◽  
Moving Object Tracking
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