Long-term real time object tracking based on multi-scale local correlation filtering and global re-detection

Zhao, Qi; Zhang, Boxue; Feng, Wenquan; Du, Zhiying; Zhang, Hong; Sun, Daniel

doi:10.1007/s00607-020-00807-8

Cited by 5 publications

(3 citation statements)

References 18 publications

(24 reference statements)

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“…For example, in the article entitled "Long-term real time object tracking based on multi-scale local correlation filtering and global re-detection" [34], the authors applied BDA techniques to visual object tracking that is one of the central research topics in the field of computer vision. In particular, the big data problem in this topic is due to the variation of the target and the surrounding environment.…”

Section: Article Overviews Of This Special Issuementioning

confidence: 99%

Software-driven big data analytics

Ranjan

Villari

et al. 2020

Computing

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Data analytics is the crucial step to reveal essential values of datasets and complete the value chain of big data. In practice, both the hardware infrastructure and the software stack play a fundamental role in big data analytics (BDA). Unfortunately, it is evident that a disproportionately larger amount of effort is being invested in the hardware infrastructure development over the software stack development. Given our concern about a software crisis brewing in the big data ecosystem, we argue that it is time to further strengthen and expand the role of software in BDA implementations. This special issue is then aimed to create a common ground and a reference point for both researchers and practitioners from multiple disciplines to discuss the rigor, relevance, experience and challenges of software-driven BDA as an emerging domain. We also expect to use this special issue to attract more attention and efforts to tighten communication and collaboration between the software engineering community and the data science community.

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Section: Article Overviews Of This Special Issuementioning

confidence: 99%

Software-driven big data analytics

Ranjan

Villari

et al. 2020

Computing

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“…The ITS has realized the intellectualization of transportation system through advanced information technology, data communication, electronic control, vehicle engineering and other technologies. It has improved the operational efficiency and safety of the transportation system, optimized the travel environment, and provided strong technical support for the sustained and healthy development of urban transportation [ 1 , 2 ]. Among numerous ITS applications, traffic flow detection is a key link.…”

Section: Introductionmentioning

confidence: 99%

Traffic flow detection method based on improved SSD algorithm for intelligent transportation system

Su,

Shu

2024

PLoS ONE

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With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system.

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“…Li et al took advantage of the discriminative scale of the discriminative-scale space tracking (DSST) algorithm to improve the effect of long-time tracking and scale adaptive tracking, but the problem of occlusion still cannot be solved well. 9 To solve the occlusion problem, an occlusion judgement is added to the DSST algorithm, and a detector is used to detect the target position when the target is occluded, 10,11 improved tracking accuracy under occlusion, but tracking performance needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

Multiple feature fusion‐based video face tracking for IoT big data

Liu

Huo

et al. 2021

Int J of Intelligent Sys

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With the advancement of Internet of Things (IoT) and artificial intelligence technologies, and the need for rapid application growth in fields, such as security entrance control and financial business trade, facial information processing has become an important means for achieving identity authentication and information security. However, in the process of acquiring facial feature information, face information is easily affected by factors, such as object occlusion, lighting changes, and similar backgrounds. In this paper, we propose a multifeature fusion algorithm based on integral histograms and a real‐time update tracking particle filtering (PF) module. First, edge features and colour features are extracted, weighting methods are used to weight the colour histogram and edge features to describe facial features, and fusion of colour features and edge features is made adaptive by using fusion coefficients to improve face tracking reliability. Then, the integral histogram is integrated into the PF algorithm to simplify the calculation steps of complex particles and improve operational efficiency. Finally, the tracking window size is adjusted in real‐time according to the change in the average distance from the particle centre to the edge of the current model and the initial model to reduce the drift problem and achieve stable tracking with significant changes in the target dimension. The results show that the algorithm improves video tracking accuracy, simplifies particle operation complexity, improves the speed, and has good anti‐interference ability and robustness compared with extracting a single feature.

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Long-term real time object tracking based on multi-scale local correlation filtering and global re-detection

Cited by 5 publications

References 18 publications

Software-driven big data analytics

Software-driven big data analytics

Traffic flow detection method based on improved SSD algorithm for intelligent transportation system

Multiple feature fusion‐based video face tracking for IoT big data

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