Author(s)

Dawei Zhang ¹, Dan Huang ²

Affiliation(s)

¹ School of Electronic Information Engineering, Beihai Vocational College, Xizang Street, Beihai, China
² School of General Education, Beihai University of Art and Design, New Century Street, Beihai, China

Corresponding Author

Dawei Zhang

ABSTRACT

The advancements in technology have facilitated the interconnection of all things, with computer vision technology emerging as a prominent field in society. As cities continue to evolve towards greater levels of intelligence, the utilization of cameras as the primary means of collecting visual data for road traffic flow monitoring has become a ubiquitous sight. The current research endeavors to devise a sophisticated vehicle flow denoising algorithm that leverages the power of multi-frame video sequences. By processing images captured from road monitoring systems, this algorithm effectively filters out noise within vehicle flow data, enabling the acquisition of more precise vehicular statistics. Consequently, it facilitates a deeper analysis of vehicle counts and densities, ultimately providing vital data support for managing urban pressures and enhancing road decongestion efforts.

KEYWORDS

Denoising, Traffic Statistics, Video Sequences

CITE THIS PAPER

Dawei Zhang, Dan Huang, Denoising of Traffic Statistics in Multi-frame Video Sequences. Journal of Image Processing Theory and Applications (2024) Vol. 7: 75-83. DOI: http://dx.doi.org/10.23977/jipta.2024.070109.

REFERENCES

[1] LI Yancheng et al. RED-MAM: A residual encoder-decoder network based on multi-attention fusion for ultrasound image denoising [J]. Biomedical Signal Processing and Control, 2023, 79(P1) 
[2] Xiao H ,Zhao Z ,Yang T .A traffic flow prediction method based on constrained dynamic graph convolutional recurrent networks[J].Engineering Applications of Artificial Intelligence, 2024, 133(PE):108486.

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