An improved adaptive background modeling algorithm based on Gaussian Mixture Model

Suo, Peng; Wang, Yanjiang

doi:10.1109/icosp.2008.4697402

Cited by 19 publications

(5 citation statements)

References 5 publications

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“…The authors of [22] modulated the learning rate of background model based on scene activity. In [23], an updating method with adaptive learning rate (we abbreviate this method as GMMX for short) is proposed to accurately segment the objects that move slow or stop for a while during moving.…”

Section: Related Researchmentioning

confidence: 99%

“…Especially, we find that many stopped cars gradually fuse into background and cannot be traced again with the previous methods. Firstly, most of these methods [14, 15, 17, 18, 21–23] perceive sudden change only based on the image information, such as illumination changes and background movements. Therefore, the accuracy of perception is difficult to be guaranteed.…”

Section: Related Researchmentioning

confidence: 99%

“…The stopped foreground objects will not fuse into background by adjusting α according traffic light signal. Then, we use GMM [8], GMMX [23], and FGD [12] as experimental subjects and traffic light signals as external information. How these methods are enhanced by our approach is described elaborately in the following text.…”

Section: Our Approachmentioning

confidence: 99%

“…To alleviate these disadvantages, numerous improvements have been proposed over the recent years. In this paper, we choose a method as the comparative experiment, which is abbreviated as GMMX [23] and has an outstanding performance on detecting temporarily stopped objects with adaptive learning rate.…”

Section: Our Approachmentioning

confidence: 99%

“…The main contribution of paper [23] is a model number adaptive method to decrease the amount of computation and an updating method with adaptive learning rate to accurately segment the objects that move slow or stop for a while during moving (here we are only interested in the second method). The authors think the fixed learning rate causes the problem that moving objects stopping for a short time will rapidly be updated to the background model by the GMM.…”

Section: Our Approachmentioning

confidence: 99%

See 4 more Smart Citations

Using Traffic Light Signal to Enhance Intersection Foreground Detection Based on Video Sensor Networks

Ding

Wang

Liu

2014

International Journal of Distributed Sensor Networks

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Foreground detection plays an important role in the traffic surveillance applications, especially in urban intersections. Background subtraction is an efficient approach to segment the background and foreground with static cameras from video sensor networks. But when modelling the background, most statistical techniques adjust the learning rate only based on the changes from video sequences, which is a crucial parameter controlling the updating speed. This causes a slow adaptation to sudden environmental changes. For example, a stopped car fuses into background before moving again, and it lowers the segmentation performance. This paper proposes an efficient way to address the problem by accounting for the physical world signal in traffic junctions. It assigns an adaptive learning rate to each pixel by integrating traffic light signal obtained from sensor networks.Combined with abundant physical world signals, background subtraction method is able to adapt itself to the outside world changes instantly. We test our approach in real urban traffic intersection; experimental results show that the new method increases the accuracy of detection and has a promising future.

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Section: Related Researchmentioning

confidence: 99%