Research on Anomaly Suppression Correlation Filtering Algorithm

Ren, Hongliang; Cui, Yin; Shi, Tao

doi:10.1109/access.2021.3138080

Cited by 2 publications

(4 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering that execution times tend to increase with a larger number of samples, λ was set to 10 for all reported experiments. During the first selection, the values of φ 1 1 , φ 1 2 , φ 1 3 , φ 1 4 , φ 1 5 and φ 1 6 were assigned as -0.75, 1, 0, 0.5, 1, and 0.25, respectively. Subsequently, for the second selection, these values were adjusted to 1, 0.25, 0.75, 1, 1, and 1, respectively, based on a significant number of repeated experiments.…”

Section: ) Implementation Detailsmentioning

confidence: 99%

“…W ITH the informatization of industry and the increase in the number of surveillance cameras, there is a strong demand for the automatic processing of captured videos. In this field, background estimation is a basic, low-level task applied as a pre-processing step for object tracking, video compression, inpainting, privacy protection, computational photography and so on [1]- [8]. Aiming at the problems of occlusion, drift, and background change in visual image tracking, Ren et al [1] presented a background learning cor-relation filtering algorithm based on multi-feature fusion.…”

Section: Introductionmentioning

confidence: 99%

“…In this field, background estimation is a basic, low-level task applied as a pre-processing step for object tracking, video compression, inpainting, privacy protection, computational photography and so on [1]- [8]. Aiming at the problems of occlusion, drift, and background change in visual image tracking, Ren et al [1] presented a background learning cor-relation filtering algorithm based on multi-feature fusion. In the framework of correlation filtering, multi-feature fusion, multi-template update, and background learning regularization are used to improve the performance of the filter in the problem of template contamination and object occlusion.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Background Modeling Based on Keyframe and Particle Shape Property for Surveillance Video

Fan,

He,

Lin

et al. 2023

IEEE Access

View full text Add to dashboard Cite

With the development of industrial informatization, video processing technology is receiving more and more attention. Extracting background is a prerequisite for many video processing techniques, so video background modeling technology is becoming highly sought-after. Currently, there are a variety of approaches to estimating background; however, many of these methods have the fault of not being able to accurately distinguish between foreground and background, especially when objects move slowly or remain still for a period of time. In this paper, a novel background modeling scheme is proposed for surveillance video, based on keyframe and particle shape properties. The model consists of three parts: the first part is to reduce running time and eliminate the ghost phenomenon caused by adjacent redundant frames by extracting keyframe and dividing the extracted frames into several groups; the second part includes three steps, computing binarized difference, characterizing the binarized difference and screening the difference where a quadruple, composed of particle shape properties, is designed to quantitatively describe binarized differences; the third part involves generating the temporary background and updating the temporary background according to the similarity of data obtained at the newly proposed locations. Experiment results on SBMnet and SBI datasets and comparisons with some emerging algorithms show that the performance of the proposed model is superior or comparable to the other state-of-the-art methods particularly when dealing with stationary objects. Furthermore, the proposed method ranks as the second for intermittent category video, compared to the other 31 state-of-art methods. Moreover, the speed of the proposed method, 5.38 Frame Per Second ( FPS ) for SBMnet dataset and 10.94 FPS for SBI dataset, is faster than most public methods.INDEX TERMS video processing, surveillance video, background modeling, difference, quadruple, keyframe, particle shape, stationary object.

show abstract

Section: ) Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Background Modeling Based on Keyframe and Particle Shape Property for Surveillance Video

Fan,

He,

Lin

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Multi-feature fusion is a method commonly used across various domains such as computer vision, natural language processing, and signal processing [7]. It entails amalgamating information from multiple sources or features to enhance a system or model's performance.…”

Section: Introductionmentioning

confidence: 99%

An English Translation Error Correction System Based on Multi-Feature Fusion

Liu

2024

jes

View full text Add to dashboard Cite

Multi-feature fusion is an advanced computational technique that combines multiple sources of information or characteristics to improve the performance of a system or model. In various fields such as computer vision, natural language processing, and machine learning, this approach integrates diverse features like visual cues, textual data, and contextual information to enhance accuracy, robustness, and efficiency. Multi-feature fusion-based English translation represents a cutting-edge approach in natural language processing, where diverse linguistic features are amalgamated to refine the accuracy and quality of translation outcomes. By integrating various elements such as syntax, semantics, context, and linguistic patterns, this method significantly enhances the translation process, ensuring more nuanced and contextually precise results. This paper introduces an innovative English Translation Error Correction System (ETECS) founded on Multi-Feature Fusion, supplemented by a bi-gram Statistical Classification Neural Network (n-gramCNN). In response to the growing demand for accurate and efficient translation error detection and correction, this research endeavors to enhance the quality of English translations through a comprehensive and advanced methodology. Using a dataset of 1,000 English text samples with corresponding translations, ETECS demonstrates an impressive error detection accuracy of 95%. Upon identifying errors, the system achieves a correction accuracy of 92%, effectively rectifying grammatical, syntactical, and semantic inconsistencies. Moreover, ETECS outperforms baseline error correction systems by a significant margin, showcasing its superiority in handling complex translation errors. The bi-gram Statistical Classification Neural Network (n-gramCNN) contributes to this success by achieving an F1 score of 0.94, highlighting its effectiveness in classifying and correcting translation errors.

show abstract

Research on Anomaly Suppression Correlation Filtering Algorithm

Cited by 2 publications

References 51 publications

A Novel Background Modeling Based on Keyframe and Particle Shape Property for Surveillance Video

A Novel Background Modeling Based on Keyframe and Particle Shape Property for Surveillance Video

An English Translation Error Correction System Based on Multi-Feature Fusion

Contact Info

Product

Resources

About