Fast background subtraction algorithm using two-level sampling and silhouette detection

Lee, Dae-Youn; Ahn, Jae-Kyun

doi:10.1109/icip.2009.5414397

Cited by 10 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Depending upon the number of foreground pixels around the sampled locations, further spatial expansion is performed. Lee et al [30] further improved upon [29] by classifying pixels in an interweaved order. They showed speedup in the range of 2.3-3.4 over [10].…”

Section: A Skip Selection Using Gmm S-mdmentioning

confidence: 97%

Skip Decision and Reference Frame Selection for Low-Complexity H.264/AVC Surveillance Video Coding

Gorur

Amrutur

2014

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

H.264/advanced video coding surveillance video encoders use the Skip mode specified by the standard to reduce bandwidth. They also use multiple frames as reference for motion-compensated prediction. In this paper, we propose two techniques to reduce the bandwidth and computational cost of static camera surveillance video encoders without affecting detection and recognition performance. A spatial sampler is proposed to sample pixels that are segmented using a Gaussian mixture model. Modified weight updates are derived for the parameters of the mixture model to reduce floating point computations. A storage pattern of the parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. The second contribution is a low computational cost algorithm to choose the reference frames. The proposed reference frame selection algorithm reduces the cost of coding uncovered background regions. We also study the number of reference frames required to achieve good coding efficiency. Distortion over foreground pixels is measured to quantify the performance of the proposed techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence.Index Terms-Cache optimization, H.264/advanced video coding (AVC), motion detection, reference frame selection, skip decision, video surveillance.

show abstract

Section: A Skip Selection Using Gmm S-mdmentioning

confidence: 97%

Skip Decision and Reference Frame Selection for Low-Complexity H.264/AVC Surveillance Video Coding

Gorur

Amrutur

2014

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

show abstract

“…In Ref. 19, MoG has been applied at subblock level and then the obtained foreground objects are refined to a silhouette detector. Other authors suggest applying the algorithm only in regions of interest that are typically smaller compared to the entire image and, in this way, it is possible to reduce the computational time of the MoG.…”

Section: Real-time Applicationmentioning

confidence: 99%

“…14). Nevertheless, these approaches are strictly related to a specific application, showing a low degree of adaptability because they are based on the modification of one characteristic of the algorithm, for example, by modifying the number of Gaussians distributions [15][16][17] or by reducing the number of operations considering a smaller images 18,19 or using a different feature space. 20 Moreover, MoG and its derivations are widely used in multicamera applications as first stage of video processing (see, for example, Refs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive background modeling in multicamera system for real-time object detection

Camplani

Salgado

2011

Opt. Eng

View full text Add to dashboard Cite

Abstract. We present an adaptive and efficient background modeling strategy for real-time object detection in multicamera systems. The proposed approach is an innovative multiparameter adaptation strategy of the mixture of Gaussian (MoG) background modeling algorithm. This approach is able to efficiently adjust the computational requirements of the tasks to the available processing power and to the activity of the scene. The innovative approach allows one to adapt the MoG without a significant loss in the detection accuracy while contemporarily adhering to the real-time constraints. The adaptation strategy works at the local level by modifying, independently, the MoG parameters of each task, and then, whenever the results of the local strategy are not satisfactory, a global adaptation strategy starts that aims at balancing the workload among the tasks. Our approach has been tested on three different data sets, including several image sizes, heterogeneous environments (indoor and outdoor scenarios), and different real-time constraints. The results show that the proposed adaptive system is well suited for multicamera applications thanks to this efficiency and adaptability; it guarantees real-time highly accurate detections. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

show abstract

“…However, their method does not update the background which results in higher false alarm. Jain et al [19] proposed a method that models the background based on a sub pixel edge map that represents the edge position and orientation using a mixture of Gaussian models. Their method has a high computational cost due to the use of increased number of Gaussians that requires update at every frame.…”

Section: Introductionmentioning

confidence: 99%

Background Modelling Using Edge-Segment Distributions

Kim

Murshed

Rivera

et al. 2013

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

We propose an edge‐segment‐based statistical background modelling algorithm to detect the moving edges for the detection of moving objects using a static camera. Traditional pixel intensity‐based background modelling algorithms face difficulties in dynamic environments since they cannot handle sudden changes in illumination. They also bring out ghosts when a sudden change occurs in the scene. To cope with this issue, intensity and noise robust edge‐based features have emerged. However, existing edge‐pixel‐based methods suffer from scattered moving edge pixels since they cannot utilize the shape. Moreover, traditional segment‐ based methods cannot handle edge shape variations and miss moving edges when they come close to the background edges. Unlike traditional approaches, our proposed method builds the background model from ordinary training frames that may contain moving objects. Furthermore, it does not leave any ghosts behind. Moreover, our method uses an automatic threshold for every background edge distribution for matching. This makes our approach robust to illumination change, camera movement and background motion. Experiments show that our method outperforms others and can detect moving edges efficiently despite the above mentioned difficulties

show abstract

Fast background subtraction algorithm using two-level sampling and silhouette detection

Cited by 10 publications

References 7 publications

Skip Decision and Reference Frame Selection for Low-Complexity H.264/AVC Surveillance Video Coding

Skip Decision and Reference Frame Selection for Low-Complexity H.264/AVC Surveillance Video Coding

Adaptive background modeling in multicamera system for real-time object detection

Background Modelling Using Edge-Segment Distributions

Contact Info

Product

Resources

About