Study of multiple moving targets’ detection in fisheye video based on the moving blob model

Wu, Jianhui; Huang, Feng; Hu, Wenjing; He, Wei; Tu, Bing; Guo, Lei; Ou, Xianfeng; Zhang, Guoyun

doi:10.1007/s11042-018-5763-5

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…The SecureStable-CA approach also utilizes the Highest-Degree [51] and Blob algorithms [52] to formulate a cluster of nearest nodes. The SecureSable-CA introduces a relative mobility metric to provide stability, improve the CH lifetime, and decrease the computation overhead of selecting a cluster.…”

Section: A Securestable-camentioning

confidence: 99%

StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

et al. 2020

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Vehicular Ad-hoc Network (VANET) is a modern era of dynamic information distribution among societies. VANET provides an extensive diversity of applications in various domains, such as Intelligent Transport System (ITS) and other road safety applications. VANET supports direct communications between vehicles and infrastructure. These direct communications cause bandwidth problems, high power consumption, and other similar issues. To overcome these challenges, clustering methods have been proposed to limit the communication of vehicles with the infrastructure. In clustering, vehicles are grouped together to formulate a cluster based on certain rules. Every cluster consists of a limited number of vehicles/nodes and a cluster head (CH). However, the significant challenge for clustering is to preserve the stability of clusters. Furthermore, a secure mechanism is required to recognize malicious and compromised nodes to overcome the risk of invalid information sharing. In the proposed approach, we address these challenges using components of trust. A trust-based clustering mechanism allows clusters to determine a trustworthy CH. The novel features incorporated in the proposed algorithm includes trust-based CH selection that comprises of knowledge, reputation, and experience of a node. Also, a backup head is determined by analyzing the trust of every node in a cluster. The major significance of using trust in clustering is the identification of malicious and compromised nodes. The recognition of these nodes helps to eliminate the risk of invalid information. We have also evaluated the proposed mechanism with the existing approaches and the results illustrate that the mechanism is able to provide security and improve the stability by increasing the lifetime of CHs and by decreasing the computation overhead of the CH re-selection. The StabTrust also successfully identifies malicious and compromised vehicles and provides robust security against several potential attacks. INDEX TERMS Intelligent transport system, security, vehicular ad-hoc networks, trust-based clustering, VANET attacks.

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Section: A Securestable-camentioning

confidence: 99%

StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

et al. 2020

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“…In addition, in terms of the multi-feature fusion strategy, the weighted fusion method is generally adopted in the researches, and some researchers use fuzzy neural network and other methods to determine and update the weights. For example, Wu et al ( 2018 ) conducted fuzzy modeling for features such as object area and shape complexity, proposed a corresponding fuzzy rule. It used a fuzzy neural network to optimize various parameters of the reasoning system to identify the object.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Feature Fusion Method in Merging Information of Heterogenous-View Data for Oil Painting Image Feature Extraction and Recognition

Chen¹,

Yang

2021

Front. Neurorobot.

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The art of oil painting reflects on society in the form of vision, while technology constantly explores and provides powerful possibilities to transform the society, which also includes the revolution in the way of art creation and even the way of thinking. The progress of science and technology often provides great changes for the creation of art, and also often changes people's way of appreciation and ideas. The oil painting image feature extraction and recognition is an important field in computer vision, which is widely used in video surveillance, human-computer interaction, sign language recognition and medical, health care. In the past few decades, feature extraction and recognition have focused on the multi-feature fusion method. However, the captured oil painting image is sensitive to light changes and background noise, which limits the robustness of feature extraction and recognition. Oil painting feature extraction is the basis of feature classification. Feature classification based on a single feature is easily affected by the inaccurate detection accuracy of the object area, object angle, scale change, noise interference and other factors, resulting in the reduction of classification accuracy. Therefore, we propose a novel multi-feature fusion method in merging information of heterogenous-view data for oil painting image feature extraction and recognition in this paper. It fuses the width-to-height ratio feature, rotation invariant uniform local binary mode feature and SIFT feature. Meanwhile, we adopt a modified faster RCNN to extract the semantic feature of oil painting. Then the feature is classified based on the support vector machine and K-nearest neighbor method. The experiment results show that the feature extraction method based on multi-feature fusion can significantly improve the average classification accuracy of oil painting and have high recognition efficiency.

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“…Broad scientific research in visual object tracking started in 1980s and is still in progress (Anishchenko, 2018;Deviatkov and Lychkov, 2017;Lychkov et al, 2018;Morozov et al, 2015;Taranyan et al, 2018) because real videos include many issues that are difficult to deal with, e. g. moving object occlusion, drastic illumination changes, video noise and blur. Main approaches to visual object tracking are blob-tracking (Wu et al, 2018), optical flow based tracking (Du et al, 2018), neural network tracking (Price et al, 2018), contour-based tracking (Allili and Ziou, 2006), 3D model based tracking (Yu et al, 2018), etc. Neural networks outperforms other approaches in terms of accuracy but they require special hardware for real-time operation.…”

Section: Introductionmentioning

confidence: 99%

Semi-Automatic Multi-Camera Video Annotation Tool for Object Tracking and Optical Flow Benchmark Creation

Devyatkov¹,

Lychkov²

2019

International Conferences Interfaces and Human Computer Interaction 2019; Game and Entertainment Technologies 2019; And Compute

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This paper addresses a problem of video annotation for object tracking evaluation. Contemporary object tracking methods relies heavily on optical flow calculation techniques. State of the art optical flow calculation methods are not enough to provide robust and accurate tracking under object occlusion and shading conditions. Thus, comprehensive visual object tracking datasets are required for deep analysis, benchmarking and improvement of object tracking methods. In most cases publicly available real video datasets does not include ground truth for optical flow evaluation, so new datasets might be in demand. Public video annotation tools for object tracking dataset labeling were reviewed. Lack of open source video annotation tools with functions of automatic optical flow markers and object bounding box visual tracking was noticed. In this work we developed a new open video annotation tool that is suitable for object bounding box labeling as well as optical flow annotation in multicamera video recordings. This tool makes it possible to specify the feature point and/or moving object position on the first video frame and track its position automatically in the subsequent video frames. This tool supports manual adjustment of the tracked position if needed. Multi-layer modular architecture of the annotation tool simplifies its testing, maintenance and further extension by the community.

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Study of multiple moving targets’ detection in fisheye video based on the moving blob model

Cited by 9 publications

References 13 publications

StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

A Novel Multi-Feature Fusion Method in Merging Information of Heterogenous-View Data for Oil Painting Image Feature Extraction and Recognition

Semi-Automatic Multi-Camera Video Annotation Tool for Object Tracking and Optical Flow Benchmark Creation

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