Dual‐rate background subtraction approach for estimating traffic queue parameters in urban scenes

Milla, Jose Manuel; Toral, S. L.; Vargas, Manuel; Barrero, F.

doi:10.1049/iet-its.2012.0020

Cited by 11 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We analyze sequences of 900 images for each activity, and the results from our proposal are compared with other methods, such as Σ∆ [22], DMD [39], MRFMD [23], DSTEI [26], Eigen-Background [30], SOBS [33], SWCD [40], ViBe [47], GMM [51] and DEU [67]. The results analysis can be seen in Table 2.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

Romero-González,

Córdova-Esparza,

Terven

et al. 2024

Algorithms

View full text Add to dashboard Cite

This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Σ∆ Background Estimation (Σ∆). In the method proposed by [22], a variance estimator is used to understand the variability of pixel intensity. This estimator is used as a threshold.…”

Section: Introductionmentioning

confidence: 99%

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

Romero-González,

Córdova-Esparza,

Terven

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…A low value for h(x, y) indicates that the background model is stable and reliable, whereas a high value for h (x, y) indicates that the background model needs to be updated for greater stability. We use a similar scheme to that employed in [23,24] to evaluate traffic flow states at the pixel level. The detection ratio d(x, y)/ f (x, y) ∈ [0, 1] is used to partition the traffic scene into 'very light,' 'light,' 'moderate,' 'heavy,' and 'very heavy' states, where d(x, y) is the number of times the pixel is in the foreground and f (x, y) is the current number of frames in the confidence period.…”

Section: Our Proposed Vehicle Detection Methodsmentioning

confidence: 99%

Counting vehicles in urban traffic scenes using foreground time‐spatial images

Zhang

Zhao

Zhang

2016

IET Intelligent Transport Systems

View full text Add to dashboard Cite

“…Another study by S. Toral showed improvements of stability [37], with the enhanced method selectively recalculating background pixels to successfully detect slow moving vehicles. Another study considered a novel algorithm for queue-parameter estimation by utilizing two background models estimating temporal stops in vehicles [38]. The method was found to be computationally efficient, with accuracy of 98%, while the main limitation was in detecting a vehicle when another vehicle passes by the same point.…”

Section: Sigma-deltamentioning

confidence: 99%

“…Sigma-delta[38] 98% full queue Failure to classify two cars as the same object because one car entered the scene simultaneously while another car left at the same point.2.1.11. Predictive background modeling Predictive procedures are employed in modeling the state dynamic of each pixel.…”

mentioning

confidence: 99%

A comprehensive review of vehicle detection using computer vision

et al. 2021

View full text Add to dashboard Cite

A crucial step in designing intelligent transport systems (ITS) is vehicle detection. The challenges of vehicle detection in urban roads arise because of camera position, background variations, occlusion, multiple foreground objects as well as vehicle pose.The current study provides a synopsis of state-of-the-art vehicle detection techniques, which are categorized according to motion and appearance-based techniques starting with frame differencing and background subtraction until feature extraction, a more complicated model in comparison. The advantages and disadvantages among the techniques are also highlighted with a conclusion as to the most accurate one for vehicle detection.This is an open access article under the CC BY-SA license.

show abstract

Dual‐rate background subtraction approach for estimating traffic queue parameters in urban scenes

Cited by 11 publications

References 22 publications

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

Counting vehicles in urban traffic scenes using foreground time‐spatial images

A comprehensive review of vehicle detection using computer vision

Contact Info

Product

Resources

About